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High resolution maps from wide angle sonar
Abstract and Figures
We describe the use of multiple wide-angle sonar range measurements to map the surroundings of an autonomous mobile robot. A sonar range reading provides information concerning empty and occupied volumes in a cone (subtending 30 degrees in our case) in front of the sensor. The reading is modelled as probability profiles projected onto a rasterized map, where somewhere occupied and everywhere empty areas are represented. Range measurements from multiple points of view (taken from multiple sensors on the robot, and from the same sensors after robot moves) are systematically integrated in the map. Overlapping empty volumes re-inforce each other, and serve to condense the range of occupied volumes. The map definition improves as more readings are added. The final map shows regions probably occupied, probably unoccupied, and unknown areas. The method deals effectively with clutter, and can be used for motion planning and for extended landmark recognition. This system has been tested on the Neptune mobile robot at CMU.
Figures - uploaded by Alberto Elfes
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... Occupancy grid maps (OGMs) are fundamental tools in mobile robotics for mapping and navigation. They represent the environment as a tessellated, multi-dimensional grid, with each cell quantifying whether a part of the environment is occupied or not [18]. This setup, given the state of the system, incorporates a probabilistic approach to account for uncertainties in measurements and the robot's pose estimation [19], [20]. ...
... OGMs are typically updated using sensor data from LiDAR, sonar, or vision systems and the robot's movements over time. This process can be employed using various frameworks, including probabilistic [18], Bayesian [21], neural network-based [22], and forward modeling approaches [23]. ...
Safe navigation in unknown environments stands as a significant challenge in the field of robotics. Control Barrier Function (CBF) is a strong mathematical tool to guarantee safety requirements. However, a common assumption in many works is that the CBF is already known and obstacles have predefined shapes. In this letter, we present a novel method called Occupancy Grid Map-based Control Barrier Function (OGM-CBF), which defines Control Barrier Function based on Occupancy Grid Maps. This enables generalization to unknown environments while generating online local or global maps of the environment using onboard perception sensors such as LiDAR or camera. With this method, the system guarantees safety via a single, continuously differentiable CBF per time step, which can be represented as one constraint in the CBF-QP optimization formulation while having an arbitrary number of obstacles with unknown shapes in the environment. This enables practical real-time implementation of CBF in both unknown and known environments. The efficacy of OGM-CBF is demonstrated in the safe control of an autonomous car in the CARLA simulator and a real-world industrial mobile robot.
... While the original EGO-Planner-v2 employs a swarm-based approach, this study implemented a single-drone method, and swarm flight related parts of the algorithm were omitted. The mapping module of the EGO-Planner-v2 relies on probabilistic occupancy grid maps (Moravec and Elfes, 1985), with depth images from the stereo camera serving as input. Furthermore, the mapping module adopts the fixed-sized circular buffers proposed by Usenko et al. (2017) for maintaining the local map. ...
During the last decade, the use of drones in forest monitoring and remote sensing has become highly popular. While most of the monitoring tasks take place in high altitudes and open air, in the last few years drones have also gained interest in under-canopy data collection. However, flying under the forest canopy is a complex task since the drone can not use Global Navigation Satellite Systems (GNSS) for positioning and it has to continually avoid obstacles, such as trees, branches, and rocks, on its path. For that reason, drone-based data collection under the forest canopy is still mainly based on manual control by human pilots. Autonomous flying in GNSS-denied obstacle-rich environment has been an actively researched topic in the field of robotics during the last years and various open-sourced methods have been published in the literature. However, most of the research is done purely from the point-of-view of robotics and only a few studies have been published in the boundary of forest sciences and robotics aiming to take steps towards autonomous forest data collection. In this study, a prototype of an autonomous under-canopy drone is developed and implemented utilizing state-of-the-art open-source methods. The prototype is utilizing the EGO-Planner-v2 trajectory planner for autonomous obstacle avoidance and VINS-Fusion for Visual-inertial-odometry based GNSS-free pose estimation. The flying performance of the prototype is evaluated by performing multiple test flights with real hardware in two different boreal forest test plots with medium and difficult densities. Furthermore, the first results of the forest data collecting performance are obtained by post-processing the data collected with a low-cost stereo camera during one test flight to a 3D point cloud and by performing diameter breast at height (DBH) estimation. In the medium-density forest, all seven test flights were successful, but in the difficult test forest, one of eight test flights failed. The RMSE of the DBH estimation was 3.86 cm (12.98 %).
... It is an integration cognition result. The concept of occupancy grid map [18] and configuration space simplified the problem of mobile robots in the past tens of years. The utilization of the signed distance field algorithm in 2D static maps efficiently offers gradient in trajectory optimization tasks. ...
Planning is complicated by the combination of perception and map information, particularly when driving in heavy traffic. Developing an extendable and efficient representation that visualizes sensor noise and provides constraints to real-time planning tasks is desirable. We aim to develop an extendable map representation offering prior to cost in planning tasks to simplify the planning process of dealing with complex driving scenarios and visualize sensor noise. In this paper, we illustrate a unified context representation empowered by a modern deep learning motion prediction model, representing statistical cognition of motion prediction for human beings. A sampling-based planner is adopted to train and compare the difference in risk map generation methods. The training tools and model structures are investigated illustrating their efficiency in this task.
... The most popular approaches to representing sensor data for SBMP collision checking often use occupancy maps [33][34][35]. OctoMaps [10] implement a probabilistic occupancy map using octrees [36] of voxels, where each voxel is considered "occupied" based on Bayesian updates computed upon each new point cloud inserted into the map. They allow for collisionchecking based on bounding-volume hierarchies, which use a branch-and-prune search to limit the set of possible points for collision checking. ...
Motion planning against sensor data is often a critical bottleneck in real-time robot control. For sampling-based motion planners, which are effective for high-dimensional systems such as manipulators, the most time-intensive component is collision checking. We present a novel spatial data structure, the collision-affording point tree (CAPT): an exact representation of point clouds that accelerates collision-checking queries between robots and point clouds by an order of magnitude, with an average query time of less than 10 nanoseconds on 3D scenes comprising thousands of points. With the CAPT, sampling-based planners can generate valid, high-quality paths in under a millisecond, with total end-to-end computation time faster than 60 FPS, on a single thread of a consumer-grade CPU. We also present a point cloud filtering algorithm, based on space-filling curves, which reduces the number of points in a point cloud while preserving structure. Our approach enables robots to plan at real-time speeds in sensed environments, opening up potential uses of planning for high-dimensional systems in dynamic, changing, and unmodeled environments.
... In this traditional context, the primary goal of the robot is to avoid any physical contact with its surroundings and navigate only through open spaces, relying on measurements from proximity sensors to detect nearby objects [3]. Afterwards, numerous strategies were developed to ensure safe navigation by means of exteroceptive data; from the creation of alternative representations of space such as 2D [4], [5], 2.5D [6], [7] or occupancy grid maps that enable robot localisation, to the use of supervised learning algorithms, specifically, semantic segmentation on data extracted from RGB, RGB-D cameras [8]- [11], as the traversability analysis problem can also be interpreted as a subclass of the generic multiclass semantic segmentation problem. Despite the limited robustness that these methods exhibit in various outdoor environmental contexts [12], [13], the former approach (extrapolated to a 3D domain) seems to be more suitable for traversability estimation. ...
This paper presents TE-NeXt, a novel and efficient architecture for Traversability Estimation (TE) from sparse LiDAR point clouds based on a residual convolution block. TE-NeXt block fuses notions of current trends such as attention mechanisms and 3D sparse convolutions. TE-NeXt aims to demonstrate high capacity for generalisation in a variety of urban and natural environments, using well-known and accessible datasets such as SemanticKITTI, Rellis-3D and SemanticUSL. Thus, the designed architecture ouperforms state-of-the-art methods in the problem of semantic segmentation, demonstrating better results in unstructured environments and maintaining high reliability and robustness in urbans environments, which leads to better abstraction. Implementation is available in a open repository to the scientific community with the aim of ensuring the reproducibility of results.
... First, a new scan is discretized, and each voxel is assigned a probability of being free and occupied. These probabilities are fused into a global map using the update laws introduced by Moravec and Elfes (1985). Upper and lower bounds on the occupancy probabilities of each voxel handle adaptation to dynamic objects. ...
This paper explores the question of creating and maintaining terrain maps in environments where the terrain changes. The specific example explored is the construction of terrain maps from 3D LiDAR measurements on an electric rope shovel. The approach extends the height grid representation of terrain to include a Hidden Markov Model in each cell, enabling confidence-based mapping of constantly changing terrain. There are inherent difficulties in this problem, including semantic labelling of the LiDAR measurements associated with machinery and determining the pose of the sensor. Solutions to both of these problems are explored. The significance of this work lies in the need for accurate terrain mapping to support autonomous machine operation.
... Generally, the smaller the beamwidth, the sharper the directivity of the sensor. The directivity of ultrasonic ranging is also related to the resonant frequency and the radiation area of the sensor [22]. Due to the directivity of ultrasonic wave propagation and the dependence of ultrasonic energy on the beam angle range, the effective angle for ultrasonic ranging is limited. ...
Coaxial rotor helicopters have great potential in civilian and commercial uses, with many advantages, but challenges remain in the accurate measurement of rotor blades’ distance to prevent blade collision. In this paper, a blade tip distance measurement method based on ultrasonic measurement window and phase triggering is proposed, and the triggering time of the transmitter is studied. Due to the complexity of the measured signal, bandpass filtering and a time-of-flight (TOF) estimation based on the power density of the received signal are utilised. The method is tested on an experimental test platform with a pair of 200 kHz ultrasonic transducers. The experimental results show that the maximum ranging error is less than 1.0% for the blade tip distance in a range of 100–1000 mm. Compared with the amplitude threshold method, the proposed TOF estimation method works well on the received signal with a low SNR and improves the ranging accuracy by about 5 mm when the blade tip distance is larger than 500 mm. This study provides a good reference for the accurate measurement of rotor blade tip distance, and gives a solution for ranging high-speed rotating objects.
... The robotic fleet consists of 6 differential-driven UGVs, which is simulated in the Stage simulator for three different environments as shown in Fig. 9: (i) a large office of size 50mˆ30m, with three separated long corridors; (ii) a smaller but more connected office of size 40mˆ20m; (iii) a subterranean cave of size 50mˆ30m. A 2D occupancy grid map is generated via the gmapping SLAM package to generate local map, with a sensor range of 8m, which provides a probabilistic representation of the environment by assigning values to each cell, i.e., whether the cell has been explored, free or occupied by obstacles [14]. Each robot navigates using the ROS navigation stack move_base, with a maximum linear velocity of 0.5m{s and angular velocity of 0.8rad{s. ...
Exploration of unknown scenes before human entry is essential for safety and efficiency in numerous scenarios, e.g., subterranean exploration, reconnaissance, search and rescue missions. Fleets of autonomous robots are particularly suitable for this task, via concurrent exploration, multi-sensory perception and autonomous navigation. Communication however among the robots can be severely restricted to only close- range exchange via ad-hoc networks. Although some recent works have addressed the problem of collaborative exploration under restricted communication, the crucial role of the human operator has been mostly neglected. Indeed, the operator may: (i) require timely update regarding the exploration progress and fleet status; (ii) prioritize certain regions; and (iii) dynamically move within the explored area; To facilitate these requests, this work proposes an interactive human-oriented online coordination framework for collaborative exploration and supervision under scarce communication (iHERO). The robots switch smoothly and optimally among fast exploration, intermittent exchange of map and sensory data, and return to the operator for status update. It is ensured that these requests are fulfilled online interactively with a pre-specified latency. Extensive large-scale human-in- the-loop simulations and hardware experiments are performed over numerous challenging scenes, which signify its performance such as explored area and efficiency, and validate its potential applicability to real-world scenarios.
... Occupancy maps are more useful for robot navigation, path planning and scene understanding since all the obstacles, free space and unknown areas in the environment are clearly marked in the map. When the robot poses used to collect the sensor information are known exactly, the evidence grid mapping technique [4,16,17] is an elegant and efficient approach to generate and update the occupancy grid map using the sensor data with the probabilistic model based on the Bayesian formulation. However, when robot navigates in an unknown environment and performs SLAM, its own poses need to be estimated and contain uncertainties. ...
In this paper, we propose an optimization based SLAM approach to simultaneously optimize the robot trajectory and the occupancy map using 2D laser scans (and odometry) information. The key novelty is that the robot poses and the occupancy map are optimized together, which is significantly different from existing occupancy mapping strategies where the robot poses need to be obtained first before the map can be estimated. In our formulation, the map is represented as a continuous occupancy map where each 2D point in the environment has a corresponding evidence value. The Occupancy-SLAM problem is formulated as an optimization problem where the variables include all the robot poses and the occupancy values at the selected discrete grid cell nodes. We propose a variation of Gauss-Newton method to solve this new formulated problem, obtaining the optimized occupancy map and robot trajectory together with their uncertainties. Our algorithm is an offline approach since it is based on batch optimization and the number of variables involved is large. Evaluations using simulations and publicly available practical 2D laser datasets demonstrate that the proposed approach can estimate the maps and robot trajectories more accurately than the state-of-the-art techniques, when a relatively accurate initial guess is provided to our algorithm. The video shows the convergence process of the proposed Occupancy-SLAM and comparison of results to Cartographer can be found at \url{https://youtu.be/4oLyVEUC4iY}.
A navigation system is described for a mobile robot equipped with a rotating ultrasonic range sensor. This navigation system is based on a dynamically maintained model of the local environment, called the composite local model. The composite local model integrates information from the rotating range sensor, the robot's touch sensor, and a pre-learned global model as the robot moves through its environment. Techniques are described for constructing a line segment description of the most recent sensor scan (the sensor model), and for integrating such descriptions to build up a model of the immediate environment (the composite local model). The estimated position of the robot is corrected by the difference in position between observed sensor signals and the corresponding symbols in the composite local model. A learning technique is described in which the robot develops a global model and a network of places. The network of places is used in global path planning, while the segments are recalled from the global model to assist in local path execution. This system is useful for navigation in a finite, pre-learned domain such as a house, office, or factory.
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A mobile robot is required to navigate around barriers in an unexplored environment. Some heuristic strategies to aid such navigation are discussed here. Being heuristic, these methods can be neither exhaustively tested nor proved effec tive in all cases. However, examples are given to demonstrate their usefulness in obstacle avoidance. In a simple case of sufficient generality, the heuristics are shown to be effective.