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Simultaneously Search and Localize Semantic Objects in Unknown Environments
Abstract
For a robot in an unknown environment to find a target semantic object, it must perform simultaneous localization and mapping (SLAM) at both geometric and semantic levels using its onboard sensors while planning and executing its motion based on the ever-updated SLAM results. In other words, the robot must simultaneously conduct localization, semantic mapping, motion planning, and execution online in the presence of sensing and motion uncertainty. This is an open problem as it intertwines semantic SLAM and adaptive online motion planning and execution under uncertainty based on perception. Moreover, the goals of the robot's motion change on the fly depending on whether and how the robot can detect the target object. We propose a novel approach to tackle the problem, leveraging semantic SLAM, Bayesian Networks, and online probabilistic motion planning. The results demonstrate our approach's effectiveness and efficiency.
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Rapid development of artificial intelligence motivates researchers to expand the capabilities of intelligent and autonomous robots. In many robotic applications, robots are required to make planning decisions based on perceptual information to achieve diverse goals in an efficient and effective way. The planning problem has been investigated in active robot vision, in which a robot analyzes its environment and its own state in order to move sensors to obtain more useful information under certain constraints. View planning, which aims to find the best view sequence for a sensor, is one of the most challenging issues in active robot vision. The quality and efficiency of view planning are critical for many robot systems and are influenced by the nature of their tasks, hardware conditions, scanning states, and planning strategies. In this paper, we first summarize some basic concepts of active robot vision, and then review representative work on systems, algorithms and applications from four perspectives: object reconstruction, scene reconstruction, object recognition, and pose estimation. Finally, some potential directions are outlined for future work.
Distributed as an open‐source library since 2013, real‐time appearance‐based mapping (RTAB‐Map) started as an appearance‐based loop closure detection approach with memory management to deal with large‐scale and long‐term online operation. It then grew to implement simultaneous localization and mapping (SLAM) on various robots and mobile platforms. As each application brings its own set of constraints on sensors, processing capabilities, and locomotion, it raises the question of which SLAM approach is the most appropriate to use in terms of cost, accuracy, computation power, and ease of integration. Since most of SLAM approaches are either visual‐ or lidar‐based, comparison is difficult. Therefore, we decided to extend RTAB‐Map to support both visual and lidar SLAM, providing in one package a tool allowing users to implement and compare a variety of 3D and 2D solutions for a wide range of applications with different robots and sensors. This paper presents this extended version of RTAB‐Map and its use in comparing, both quantitatively and qualitatively, a large selection of popular real‐world datasets (e.g., KITTI, EuRoC, TUM RGB‐D, MIT Stata Center on PR2 robot), outlining strengths, and limitations of visual and lidar SLAM configurations from a practical perspective for autonomous navigation applications.
Due to the development of the computer vision, machine learning and deep learning technologies, the research community focuses not only on the traditional SLAM problems, such as geometric mapping and localization, but also on semantic SLAM. In this paper we propose a Semantic SLAM system which builds the semantic maps with object-level entities, and it is integrated into the RGB-D SLAM framework. The system combines object detection module that is realized by the deep-learning method, and localization module with RGB-D SLAM seamlessly. In the proposed system, object detection module is used to perform object detection and recognition, and localization module is utilized to get the exact location of the camera. The two modules are integrated together to obtain the semantic maps of the environment. Furthermore, to improve the computational efficiency of the framework, an improved Octomap based on Fast Line Rasterization Algorithm is constructed. Meanwhile, for the sake of accuracy and robustness of the semantic map, Conditional Random Field (CRF) is employed to do the optimization. Finally, we evaluate our Semantic SLAM through three different tasks, i.e. Localization, Object Detection and Mapping. Specifically, the accuracy of localization and the mapping speed are evaluated on TUM dataset. Compared with ORB-SLAM2 and original RGB-D SLAM, our system respectively get 72.9% and 91.2% improvements in dynamic environments localization evaluated by root-mean-square error (RMSE). With the improved Octomap, the proposed Semantic SLAM is 66.5% faster than the original RGB-D SLAM. We also demonstrate the efficiency of object detection through quantitative evaluation in an automated inventory management task on a real-world datasets recorded over a realistic office.
We present a unified probabilistic framework for simultaneous trajectory estimation and planning. Estimation and planning problems are usually considered separately, however, within our framework we show that solving them simultaneously can be more accurate and efficient. The key idea is to compute the full continuous-time trajectory from start to goal at each time-step. While the robot traverses the trajectory, the history portion of the trajectory signifies the solution to the estimation problem, and the future portion of the trajectory signifies a solution to the planning problem. Building on recent probabilistic inference approaches to continuous-time localization and mapping and continuous-time motion planning, we solve the joint problem by iteratively recomputing the maximum a posteriori trajectory conditioned on all available sensor data and cost information. Our approach can contend with high-degree-of-freedom trajectory spaces, uncertainty due to limited sensing capabilities, model inaccuracy, the stochastic effect of executing actions, and can find a solution in real-time. We evaluate our framework empirically in both simulation and on a mobile manipulator.
Dynamic environments have obstacles that unpredictably appear, disappear, or move. We present the first sampling-based replanning algorithm that is asymptotically optimal and single-query (designed for situation in which a priori offline computation is unavailable). Our algorithm, RRTX, refines and repairs the same search-graph over the entire duration of navigation (in contrast to previous single-query replanning algorithms that prune and then regrow some or all of the search-tree). Whenever obstacles change and/or the robot moves, a graph rewiring cascade quickly remodels the existing search-graph and repairs its shortest-path-to-goal sub-tree to reflect the new information. Both graph and tree are built directly in the robot’s state-space; thus, the resulting plan(s) respect the kinematics of the robot and continue to improve during navigation. RRTX is probabilistically complete and makes no distinction between local and global planning, yet it reacts quickly enough for real-time high-speed navigation through unpredictably changing environments. Low information transfer time is essential for enabling RRTX to react quickly in dynamic environments; we prove that the information transfer time required to inform a graph of size n about an ε-cost decrease is O(n log n) for RRTX—faster than other current asymptotically optimal single-query algorithms (we prove RRT* is and RRT# is (n log2n)). In static environments RRTX has the same amortized runtime as RRT and RRT*, Θ(log n), and is faster than RRT#, (log2n). In order to achieve O(log n) iteration time, each node maintains a set of O(log n) expected neighbors, and the search-graph maintains ε-consistency for a predefined ε. Experiments and simulations confirm our theoretical analysis and demonstrate that RRTX is useful in both static and dynamic environments.
. This paper addresses the problem of building large-scale geometric maps of indoor environments with mobile robots. It poses the map building problem as a constrained, probabilistic maximum-likelihood estimation problem. It then devises a practical algorithm for generating the most likely map from data, alog with the most likely path taken by the robot. Experimental results in cyclic environments of size up to 80 by 25 meter illustrate the appropriateness of the approach. Keywords: Bayes rule, expectation maximization, mobile robots, navigation, localization, mapping, maximum likelihood estimation, positioning, probabilistic reasoning 1. Introduction Over the last two decades or so, the problem of acquiring maps in indoor environments has received considerable attention in the mobile robotics community. The problem of map building is the problem of determining the location of entities-of-interest (such as: landmarks, obstacles), often relative to a global frame of reference (such as ...
Active SLAM poses the challenge for an autonomous robot to plan efficient paths simultaneous to the SLAM process. The uncertainties of the robot, map and sensor measurements, and the dynamic and motion constraints need to be considered in the planning process. In this paper, the active SLAM problem is formulated as an optimal trajectory planning problem. A novel technique is introduced that utilises an attractor combined with local planning strategies such as model predictive control (a.k.a. receding horizon) to solve this problem. An attractor provides high level task intentions and incorporates global information about the environment for the local planner, thereby eliminating the need for costly global planning with longer horizons. It is demonstrated that trajectory planning with an attractor results in improved performance over systems that have local planning alone
This paper describes a computational approach to edge detection. The success of the approach depends on the definition of a comprehensive set of goals for the computation of edge points. These goals must be precise enough to delimit the desired behavior of the detector while making minimal assumptions about the form of the solution. We define detection and localization criteria for a class of edges, and present mathematical forms for these criteria as functionals on the operator impulse response. A third criterion is then added to ensure that the detector has only one response to a single edge. We use the criteria in numerical optimization to derive detectors for several common image features, including step edges. On specializing the analysis to step edges, we find that there is a natural uncertainty principle between detection and localization performance, which are the two main goals. With this principle we derive a single operator shape which is optimal at any scale. The optimal detector has a simple approximate implementation in which edges are marked at maxima in gradient magnitude of a Gaussian-smoothed image. We extend this simple detector using operators of several widths to cope with different signal-to-noise ratios in the image. We present a general method, called feature synthesis, for the fine-to-coarse integration of information from operators at different scales. Finally we show that step edge detector performance improves considerably as the operator point spread function is extended along the edge.
This paper presents an integrated approach to exploration, mapping, and localization. Our algorithm uses a highly efficient Rao-Blackwellized particle filter to represent the posterior about maps and poses. It applies a decision-theoretic framework which simultaneously considers the uncertainty in the map and in the pose of the vehicle to evaluate potential actions. Thereby, it trades off the cost of executing an action with the expected information gain and takes into account possible sensor measurements gathered along the path taken by the robot. We furthermore describe how to utilize the properties of the Rao-Blackwellization to efficiently compute the expected information gain. We present experimental results obtained in the real world and in simulation to demonstrate the effectiveness of our approach.
Visual odometry (VO) is the process of estimating the egomotion of an agent (e.g., vehicle, human, and robot) using only the input of a single or multiple cameras attached to it. Application domains include robotics, wearable computing, augmented reality, and automotive. The term VO was coined in 2004 by Nister in his landmark paper [1]. The term was chosen for its similarity to wheel odometry, which incrementally estimates the motion of a vehicle by integrating the number of turns of its wheels over time. Likewise, VO operates by incrementally estimating the pose of the vehicle through examination of the changes that motion induces on the images of its onboard cameras. For VO to work effectively, there should be sufficient illumination in the environment and a static scene with enough texture to allow apparent motion to be extracted. Furthermore, consecutive frames should be captured by ensuring that they have sufficient scene overlap.
This paper introduces a novel and general real-time adaptive motion planning (RAMP) approach suitable for planning trajectories of high-DOF or redundant robots, such as mobile manipulators, in dynamic environments with moving obstacles of unknown trajectories. The RAMP approach enables simultaneous path and trajectory planning and simultaneous planning and execution of motion in real time. It facilitates real-time optimization of trajectories under various optimization criteria, such as minimizing energy and time and maximizing manipulability. It also accommodates partially specified task goals of robots easily. The approach exploits redundancy in redundant robots (such as locomotion versus manipulation in a mobile manipulator) through loose coupling of robot configuration variables to best achieve obstacle avoidance and optimization objectives. The RAMP approach has been implemented and tested in simulation over a diverse set of task environments, including environments with multiple mobile manipulators. The results (and also the accompanying video) show that the RAMP planner, with its high efficiency and flexibility, not only handles a single mobile manipulator well in dynamic environments with various obstacles of unknown motions in addition to static obstacles, but can also readily and effectively plan motions for each mobile manipulator in an environment shared by multiple mobile manipulators and other moving obstacles.
Real-time dynamic programming (RTDP) is a heuris- tic search algorithm for solving MDPs. We present a modified algorithm called Focused RTDP with several improvements. While RTDP maintains only an upper bound on the long-term reward function, FRTDP main- tains two-sided bounds and bases the output policy on the lower bound. FRTDP guides search with a new rule for outcome selection, focusing on parts of the search graph that contribute most to uncertainty about the values of good policies. FRTDP has modified trial termination cri- teria that should allow it to solve some problems (within ) that RTDP cannot. Experiments show that for all the problems we studied, FRTDP significantly outperforms RTDP and LRTDP, and converges with up to six times fewer backups than the state-of-the-art HDP algorithm.
Rao—Blackwellized particle filters (RBPFs) are an implementation of sequential Bayesian filtering that has been successfully applied to mobile robot simultaneous localization and mapping (SLAM) and exploration. Measuring the uncertainty of the distribution estimated by a RBPF is required for tasks such as information gain-guided exploration or detecting loop closures in nested loop environments. In this paper we propose a new measure that takes the uncertainty in both the robot path and the map into account. Our approach relies on the entropy of the expected map (EM) of the RBPF, a new variable built by integrating the map hypotheses from all of the particles. Unlike previous works that use the joint entropy of the RBPF for active exploration, our proposal is better suited to detect opportunities to close loops, a key aspect to reduce the robot path uncertainty and consequently to improve the quality of the maps being built. We provide a theoretical discussion and experimental results with real data that support our claims.
Computing kinodynamically feasible motion plans and repairing them on-the-fly as the environment changes is a challenging, yet relevant problem in robot navigation. We propose an online single-query sampling-based motion re-planning algorithm using finite-time invariant sets, commonly referred to as “ funnels”. We combine concepts from nonlinear systems analysis, sampling-based techniques, and graph-search methods to create a single framework that enables feedback motion re-planning for any general nonlinear dynamical system in dynamic workspaces. A volumetric network of funnels is constructed in the configuration space using sampling-based methods and invariant set theory; and an optimal sequencing of funnels from robot configuration to a desired goal region is then determined by computing the shortest-path subgraph (tree) in the network. Analyzing and formally quantifying the stability of trajectories using Lyapunov level-sets ensures kinodynamic feasibility and guaranteed set-invariance of the solution paths. Though not required, our method is capable of using a pre-computed library of motion primitives to speedup online computation of controllable motion plans that are volumetric in nature. We introduce a novel directed-graph data structure to represent the funnel-network and its inter-sequencibility; helping us leverage discrete graph-based incremental search to quickly rewire feasible and controllable motion plans on-the-fly in response to changes in the environment. We validate our approach on a simulated cart-pole, car-like robot, and 6DOF quadrotor platform in a variety of scenarios within a maze and a random forest environment. Using Monte Carlo methods, we evaluate the performance in terms of algorithm success, length of traversed trajectory, and runtime.
Active simultaneous localization and mapping (SLAM) is the problem of planning and controlling the motion of a robot to build the most accurate and complete model of the surrounding environment. Since the first foundational work in active perception appeared, more than three decades ago, this field has received increasing attention across different scientific communities. This has brought about many different approaches and formulations, and makes a review of the current trends necessary and extremely valuable for both new and experienced researchers. In this article, we survey the state of the art in active SLAM and take an in-depth look at the open challenges that still require attention to meet the needs of modern applications. After providing a historical perspective, we present a unified problem formulation and review the well-established modular solution scheme, which decouples the problem into three stages that identify, select, and execute potential navigation actions. We then analyze alternative approaches, including belief-space planning and deep reinforcement learning techniques, and review related work on multirobot coordination. This article concludes with a discussion of new research directions, addressing reproducible research, active spatial perception, and practical applications, among other topics.
Maintaining a semantic map of a complex and dynamic environment, where the uncertainty originates in both noisy perception and unexpected changes, is a challenging problem. In particular, we focus on the problem of maintaining a semantic map of an environment by a mobile agent. In this paper we address this problem in an hierarchical fashion. Firstly, we employ a probabilistic logic model representing the semantic map, as well as the associated uncertainty. Secondly, we model the interaction of the robot with the environment with a set of information-reward POMDP models, one for each partition of the environment (e.g., a room). The partition is performed in order to address the scalability limitations of POMDP models over very large state spaces. We then use probabilistic inference to determine which POMDP and policy to execute next. Experimental results show the efficiency of this architecture in real domestic service robotic scenarios.
Loop closure is necessary for correcting errors accumulated in simultaneous localization and mapping (SLAM) in unknown environments. However, conventional loop closure methods based on low-level geometric or image features may cause high ambiguity by not distinguishing similar scenarios. Thus, incorrect loop closures can occur. Though semantic 2D image information is considered in some literature to detect loop closures, there is little work that compares 3D scenes as an integral part of a semantic SLAM system. This letter introduces an approach, called SmSLAM+LCD, integrated into a semantic SLAM system to combine high-level 3D semantic information and low-level feature information to conduct accurate loop closure detection and effective drift reduction. The effectiveness of our approach is demonstrated in testing results.
To make service robots plan a whole task execution sequence and deal with task failure intelligently in the face of object occlusion and incomplete home environment information, a hybrid offline and online task planning strategy is proposed. We establish object-level semantic map and object location probabilistic relations between dynamic and static objects. Semantic mapping helps to obtain semantic locations of static objects, and the probabilistic relationship between dynamic and static objects can obtain semantic locations of dynamic objects through probabilistic reasoning. Probabilistic planning domain definition language (PPDDL) can generate offline action sequences, while partially observable Markov decision process (POMDP) can generate online action sequences. Therefore, a hybrid task planner that can receive semantic location information is constructed to generate offline and online action sequences, and realizes the dynamic switching of the two kinds of sequences through the designed planner switching mechanism. In order to improve the robustness and intelligence of robot task planning, a task replanning mechanism considering the position relationship between robot and candidate static objects is designed. Experimental results in real environment and simulation environment show that this approach can effectively increase the intelligence of task planning and improve the robustness and efficiency of robot task execution.
Humans are able to form a complex mental model of the environment they move in. This mental model captures geometric and semantic aspects of the scene, describes the environment at multiple levels of abstractions (e.g., objects, rooms, buildings), includes static and dynamic entities and their relations (e.g., a person is in a room at a given time). In contrast, current robots’ internal representations still provide a partial and fragmented understanding of the environment, either in the form of a sparse or dense set of geometric primitives (e.g., points, lines, planes, and voxels), or as a collection of objects. This article attempts to reduce the gap between robot and human perception by introducing a novel representation, a 3D dynamic scene graph (DSG), that seamlessly captures metric and semantic aspects of a dynamic environment. A DSG is a layered graph where nodes represent spatial concepts at different levels of abstraction, and edges represent spatiotemporal relations among nodes. Our second contribution is Kimera, the first fully automatic method to build a DSG from visual–inertial data. Kimera includes accurate algorithms for visual–inertial simultaneous localization and mapping (SLAM), metric–semantic 3D reconstruction, object localization, human pose and shape estimation, and scene parsing. Our third contribution is a comprehensive evaluation of Kimera in real-life datasets and photo-realistic simulations, including a newly released dataset, uHumans2, which simulates a collection of crowded indoor and outdoor scenes. Our evaluation shows that Kimera achieves competitive performance in visual–inertial SLAM, estimates an accurate 3D metric–semantic mesh model in real-time, and builds a DSG of a complex indoor environment with tens of objects and humans in minutes. Our final contribution is to showcase how to use a DSG for real-time hierarchical semantic path-planning. The core modules in Kimera have been released open source.
2020 IEEE. We provide an open-source C++ library for real-time metric-semantic visual-inertial Simultaneous Localization And Mapping (SLAM). The library goes beyond existing visual and visual-inertial SLAM libraries (e.g., ORB-SLAM, VINS-Mono, OKVIS, ROVIO) by enabling mesh reconstruction and semantic labeling in 3D. Kimera is designed with modularity in mind and has four key components: a visual-inertial odometry (VIO) module for fast and accurate state estimation, a robust pose graph optimizer for global trajectory estimation, a lightweight 3D mesher module for fast mesh reconstruction, and a dense 3D metric-semantic reconstruction module. The modules can be run in isolation or in combination, hence Kimera can easily fall back to a state-of-the-art VIO or a full SLAM system. Kimera runs in real-time on a CPU and produces a 3D metric-semantic mesh from semantically labeled images, which can be obtained by modern deep learning methods. We hope that the flexibility, computational efficiency, robustness, and accuracy afforded by Kimera will build a solid basis for future metric-semantic SLAM and perception research, and will allow researchers across multiple areas (e.g., VIO, SLAM, 3D reconstruction, segmentation) to benchmark and prototype their own efforts without having to start from scratch.
This article develops novel strategies for optimal planning with semantic observations using continuous state partially observable Markov decision processes (CPOMDPs). Two major innovations are presented in relation to Gaussian mixture (GM) CPOMDP policy approximation methods. While existing methods have many desirable theoretical properties, they are unable to efficiently represent and reason over hybrid continuous-discrete probabilistic models. The first major innovation is the derivation of closed-form variational Bayes GM approximations of point-based value iteration Bellman policy backups, using softmax models of continuous-discrete semantic observation probabilities. A key benefit of this approach is that dynamic decision making tasks can be performed with complex non-Gaussian uncertainties, while also exploiting continuous dynamic state-space models (thus avoiding cumbersome and costly discretization). The second major innovation is a new clustering-based technique for mixture condensation that scales well to very large GM policy functions and belief functions. Simulation results for a target search and interception task with semantic observations show that the GM policies resulting from these innovations are more effective than those produced by other state-of-the-art policy approximations, but require significantly less modeling overhead and online runtime cost. Additional results show the robustness of this approach to model errors and scaling to higher dimensions.
In this paper, we present a method for single image three-dimensional (3-D) cuboid object detection and multiview object simultaneous localization and mapping in both static and dynamic environments, and demonstrate that the two parts can improve each other. First, for single image object detection, we generate high-quality cuboid proposals from two-dimensional (2-D) bounding boxes and vanishing points sampling. The proposals are further scored and selected based on the alignment with image edges. Second, multiview bundle adjustment with new object measurements is proposed to jointly optimize poses of cameras, objects, and points. Objects can provide long-range geometric and scale constraints to improve camera pose estimation and reduce monocular drift. Instead of treating dynamic regions as outliers, we utilize object representation and motion model constraints to improve the camera pose estimation. The 3-D detection experiments on SUN RGBD and KITTI show better accuracy and robustness over existing approaches. On the public TUM, KITTI odometry and our own collected datasets, our SLAM method achieves the state-of-the-art monocular camera pose estimation and at the same time, improves the 3-D object detection accuracy.
Exploration is an important aspect of robotics, whether it is for mapping, rescue missions or path planning in an unknown environment. Frontier Exploration planning (FEP) and Receding Horizon Next-Best-View planning (RH-NBVP) are two different approaches with different strengths and weaknesses. FEP explores a large environment consisting of separate regions with ease, but is slow at reaching full exploration due to moving back and forth between regions. RH-NBVP shows great potential and efficiently explores individual regions, but has the disadvantage that it can get stuck in large environments not exploring all regions. In this work we present a method that combines both approaches, with FEP as a global exploration planner and RH-NBVP for local exploration. We also present techniques to estimate potential information gain faster, to cache previously estimated gains and to exploit these to efficiently estimate new queries.
In this paper, we use 2D object detections from multiple views to simultaneously estimate a 3D quadric surface for each object and localize the camera position. We derive a SLAM formulation that uses dual quadrics as 3D landmark representations, exploiting their ability to compactly represent the size, position and orientation of an object, and show how 2D object detections can directly constrain the quadric parameters via a novel geometric error formulation. We develop a sensor model for object detectors that addresses the challenge of partially visible objects, and demonstrate how to jointly estimate the camera pose and constrained dual quadric parameters in factor graph based SLAM with a general perspective camera.
Recent years have seen significant technical progress on robot planning, enabling robots to compute actions and motions to accomplish challenging tasks involving driving, flying, walking, or manipulating objects. However, robots that have been commercially deployed in the real world typically have no or minimal planning capability. These robots are often manually programmed, teleoperated, or programmed to follow simple rules. Although these robots are highly successful in their respective niches, a lack of planning capabilities limits the range of tasks for which currently deployed robots can be used. In this article, we highlight key conclusions from a workshop sponsored by the National Science Foundation in October 2013 that summarize opportunities and key challenges in robot planning and include challenge problems identified in the workshop that can help guide future research toward making robot planning more deployable in the real world.
The task of building a map of an unknown environment and concurrently using that map to navigate is a central problem in mobile robotics research. This paper addresses the problem of how to perform concurrent mapping and localization (CML) adaptively using sonar. Stochastic mapping is a feature-based approach to CML that generalizes the extended Kalman filter to incorporate vehicle localization and environmental mapping. The authors describe an implementation of stochastic mapping that uses a delayed nearest neighbor data association strategy to initialize new features into the map, match measurements to map features, and delete out-of-date features. The authors introduce a metric for adaptive sensing that is defined in terms of Fisher information and represents the sum of the areas of the error ellipses of the vehicle and feature estimates in the map. Predicted sensor readings and expected dead-reckoning errors are used to estimate the metric for each potential action of the robot, and the action that yields the lowest cost (i.e., the maximum information) is selected. This technique is demonstrated via simulations, in-air sonar experiments, and underwater sonar experiments. Results are shown for (1) adaptive control of motion and (2) adaptive control of motion and scanning. The vehicle tends to explore selectively different objects in the environment. The performance of this adaptive algorithm is shown to be superior to straight-line motion and random motion.
We present a real-time object-based SLAM system that leverages the largest
object database to date. Our approach comprises two main components: 1) a
monocular SLAM algorithm that exploits object rigidity constraints to improve
the map and find its real scale, and 2) a novel object recognition algorithm
based on bags of binary words, which provides live detections with a database
of 500 3D objects. The two components work together and benefit each other: the
SLAM algorithm accumulates information from the observations of the objects,
anchors object features to especial map landmarks and sets constrains on the
optimization. At the same time, objects partially or fully located within the
map are used as a prior to guide the recognition algorithm, achieving higher
recall. We evaluate our proposal on five real environments showing improvements
on the accuracy of the map and efficiency with respect to other
state-of-the-art techniques.
This paper reports on an integrated navigation algorithm for the visual simultaneous localization and mapping (SLAM) robotic area coverage problem. In the robotic area coverage problem, the goal is to explore and map a given target area in a reasonable amount of time. This goal necessitates the use of minimally redundant overlap trajectories for coverage efficiency; however, visual SLAM's navigation estimate will inevitably drift over time in the absence of loop-closures. Therefore, efficient area coverage and good SLAM navigation performance represent competing objectives. To solve this decision-making problem, we introduce perception-driven navigation (PDN), an integrated navigation algorithm that automatically balances between exploration and revisitation using a reward framework. This framework accounts for vehicle localization uncertainty, area coverage performance, and the identification of good candidate regions in the environment for loop-closure. Results are shown for a hybrid simulation using synthetic and real imagery from an autonomous underwater ship hull inspection application.
The purpose of this paper is to discuss the asymptotic behavior of the sequence (f sub n(i)) generated by a nonlinear recurrence relation. This problem arises in connection with an equipment replacement problem, cf. S. Dreyfus, A Note on an Industrial Replacement Process.
We present a replanning algorithm for repairing rapidly-exploring random trees when changes are made to the configuration space. Instead of abandoning the current RRT, our algorithm efficiently removes just the newly-invalid parts and maintains the rest. It then grows the resulting tree until a new solution is found. We use this algorithm to create a probabilistic analog to the widely-used D* family of deterministic algorithms, and demonstrate its effectiveness in a multirobot planning domain
Automatically building maps from sensor data is a necessary and fundamental skill for mobile robots; as a result, considerable research attention has focused on the technical challenges inherent in the mapping problem. While statistical inference tech- niques have led to computationally efficient mapping algorithms, the next major challenge in robotic map- ping is to automate the data collection process. In this paper, we address the problem of how a robot should plan to explore an unknown environment and collect data in order to maximize the accuracy of the resulting map. We formulate exploration as a constrained optimization problem and use reinforce- ment learning to find trajectories that lead to accurate maps. We demonstrate this process in simulation and show that the learned policy not only results in improved map-building, but that the learned policy also transfers successfully to a real robot exploring on MIT campus.
There are situations in which one would like to know a good sequence of range-image views for obtaining a complete model of a scene. This paper describes two algorithms which use partial octree models to determine the "best" next view to take.
Mobile robots often operate in domains that are only incompletely known, for example, when they have to move from given start coordinates to given goal coordinates in unknown terrain. In this case, they need to be able to replan quickly as their knowledge of the terrain changes. Stentz' Focussed Dynamic A* (D*) is a heuristic search method that repeatedly determines a shortest path from the current robot coordinates to the goal coordinates while the robot moves along the path. It is able to replan faster than planning from scratch since it modifies its previous search results locally. Consequently, it has been extensively used in mobile robotics. In this article, we introduce an alternative to D* that determines the same paths and thus moves the robot in the same way but is algorithmically different. D* Lite is simple, can be rigorously analyzed, extendible in multiple ways, and is at least as efficient as D*. We believe that our results will make D*-like replanning methods even more popular and enable robotics researchers to adapt them to additional applications.
Mobile robots often find themselves in a situation where they must find a trajectory to another position in their environment, subject to constraints posed by obstacles and the capabilities of the robot itself. This is the problem of planning a path through a continuous domain, for which several approaches have been developed. Each has some limitations however, including requiring state discretizations, steep e#ciency vs. accuracy tradeo#s, or the di#culty of adding interleaved execution. Rapidly-Exploring Random Trees (RRTs) are a recently developed representation on which fast continuous domain path planners can be based. In this work, we build a path planning system based on RRTs that interleaves planning and execution, first evaluating it in simulation and then applying it to physical robots. Our planning algorithm, ERRT (execution extended RRT), introduces two novel extensions of previous RRT work, the waypoint cache and adaptive cost penalty search, which improve replanning e#ciency and the quality of generated paths. ERRT is successfully applied to a real-time multi-robot system. Results demonstrate that ERRT is significantly more e#cient for replanning than a basic RRT planner, performing competitively with or better than existing heuristic and reactive real-time path planning approaches. ERRT is a significant step forward with the potential for making path planning common on real robots, even in challenging continuous, highly dynamic domains.
The task of planning trajectories for a mobile robot has received considerable attention in the research literature. Most of the work assumes the robot has a complete and accurate model of its environment before it begins to move; less attention has been paid to the problem of partially known environments. This situation occurs for an exploratory robot or one that must move to a goal location without the benefit of a floorplan or terrain map. Existing approaches plan an initial path based on known information and then modify the plan locally or replan the entire path as the robot discovers obstacles with its sensors, sacrificing optimality or computational efficiency respectively. This paper introduces a new algorithm, D*, capable of planning paths in unknown, partially known, and changing environments in an efficient, optimal, and complete manner. 1.0 Introduction The research literature has addressed extensively the motion planning problem for one or more robots moving through a ...
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