Giorgio Grisetti

University of Freiburg | Albert-Ludwigs-Universität Freiburg · Department of Computer Science

LiDAR odometry is the task of estimating the ego-motion of the sensor from sequential laser scans. This problem has been addressed by the community for more than two decades, and many effective solutions are available nowadays. Most of these systems implicitly rely on assumptions about the operating environment, the sensor used, and motion pattern....

Accurate localization in diverse environments is a fundamental challenge in computer vision and robotics. The task involves determining a sensor's precise position and orientation, typically a camera, within a given space. Traditional localization methods often rely on passive sensing, which may struggle in scenarios with limited features or dynami...

Factor graphs are a very powerful graphical representation, used to model many problems in robotics. They are widely spread in the areas of Simultaneous Localization and Mapping (SLAM), computer vision, and localization. However, the physics of many real-world problems is better modeled through constraints, e.g., estimation in the presence of incon...

Modern visual perception techniques often rely on multiple heterogeneous sensors to achieve accurate and robust estimates. Knowledge of their relative positions is a mandatory prerequisite to accomplish sensor fusion. Typically, this result is obtained through a calibration procedure that correlates the sensors’ measurements. In this context, we fo...

Most commercially available Light Detection and Ranging (LiDAR)s measure the distances along a 2D section of the environment by sequentially sampling the free range along directions centered at the sensor’s origin. When the sensor moves during the acquisition, the measured ranges are affected by a phenomenon known as “skewing”, which appears as a d...

Factor graphs are a very powerful graphical representation, used to model many problems in robotics. They are widely spread in the areas of Simultaneous Localization and Mapping (SLAM), computer vision, and localization. In this paper we describe an approach to fill the gap with other areas, such as optimal control, by presenting an extension of Fa...

The joint optimization of the sensor trajectory and 3D map is a crucial characteristic of Simultaneous Localization and Mapping SLAM systems. To achieve this, the gold standard is Bundle Adjustment (BA). Modern 3D LiDARs now retain higher resolutions that enable the creation of point cloud images resembling those taken by conventional cameras. Neve...

Agricultural robots have the prospect to enable more efficient and sustainable agricultural production of food, feed, and fiber. Perception of crops and weeds is a central component of agricultural robots that aim to monitor fields and assess the plants as well as their growth stage in an automatic manner. Semantic perception mostly relies on deep...

Most commercially available Light Detection and Ranging (LiDAR)s measure the distances along a 2D section of the environment by sequentially sampling the free range along directions centered at the sensor's origin. When the sensor moves during the acquisition, the measured ranges are affected by a phenomenon known as skewing, which appears as a dis...

Factor graphs are graphical models used to represent a wide variety of problems across robotics, such as Structure from Motion (SfM), Simultaneous Localization and Mapping (SLAM) and calibration. Typically, at their core, they have an optimization problem whose terms only depend on a small subset of variables. Factor graph solvers exploit the local...

Pose graph optimization is a non-convex optimization problem encountered in many areas of robotics perception. Its convergence to an accurate solution is conditioned by two factors: the non-linearity of the cost function in use and the initial configuration of the pose variables. In this paper, we present HiPE, a novel hierarchical algorithm for po...

Ego-motion estimation is a fundamental building block of any autonomous system that needs to navigate in an environment. In large-scale outdoor scenes, 3D LiDARs are often used for this task, as they provide a large number of range measurements at high precision. In this paper, we propose a novel approach that exploits the intensity channel of 3D L...

Reliable and accurate registration of point clouds is a challenging problem in robotics as well as in the domain of autonomous driving. In this article, we address the task of aligning point clouds with low overlap, containing moving objects, and without prior information about the initial guess. We enhance classical ICP-based registration with neu...

A software architecture defines the blueprints of a large computational system, and is thus a crucial part of the design and development effort. This task has been explored extensively in the context of mobile robots, resulting in a plethora of reference designs and implementations. As the software architecture defines the framework in which all co...

A sensor is a device that converts a physical parameter or an environmental characteristic (e.g., temperature, distance, speed, etc.) into a signal that can be digitally measured and processed to perform specific tasks. Mobile robots need sensors to measure properties of their environment, thus allowing for safe navigation, complex perception and c...

Simultaneous Localization and Mapping (SLAM) systems are fundamental building blocks for any autonomous robot navigating in unknown environments. The SLAM implementation heavily depends on the sensor modality employed on the mobile platform. For this reason, assumptions on the scene's structure are often made to maximize estimation accuracy. This p...

Factor graphs are graphical models used to represent a wide variety of problems across robotics, such as Structure from Motion (SfM), Simultaneous Localization and Mapping (SLAM) and calibration. Typically, at their core, they have an optimization problem whose terms only depend on a small subset of variables. Factor graph solvers exploit the local...

Robots and autonomous systems need to know where they are within a map to navigate effectively. Thus, simultaneous localization and mapping or SLAM is a common building block of robot navigation systems. When building a map via a SLAM system, robots need to re-recognize places to find loop closure and reduce the odometry drift. Image-based place re...

The state-of-the-art modern pose-graph optimization (PGO) systems are vertex based. In this context, the number of variables might be high, albeit the number of cycles in the graph (loop closures) is relatively low. For sparse problems particularly, the cycle space has a significantly smaller dimension than the number of vertices. By exploiting thi...

Pose graph optimization is a non-convex optimiza- tion problem encountered in many areas of robotics perception. Its convergence to an accurate solution is conditioned by two factors: the non-linearity of the cost function in use and the initial configuration of the pose variables. In this paper, we present HiPE, a novel hierarchical algorithm for...

Nowadays, Nonlinear Least-Squares embodies the foundation of many Robotics and Computer Vision systems. The research community deeply investigated this topic in the last few years, and this resulted in the development of several open-source solvers to approach constantly increasing classes of problems. In this work, we propose a unified methodology...

Nowadays, SLAM (Simultaneous Localization and Mapping) is considered by the Robotics community to be a mature field. Currently, there are many open-source systems that are able to deliver fast and accurate estimation in typical real-world scenarios. Still, all these systems often provide an ad-hoc implementation that entailed to predefined sensor c...

Nowadays, Non-Linear Least-Squares embodies the foundation of many Robotics and Computer Vision systems. The research community deeply investigated this topic in the last years, and this resulted in the development of several open-source solvers to approach constantly increasing classes of problems. In this work, we propose a unified methodology to...

Pose-graph optimization (PGO) is a well-known problem in the robotics community. Optimizing a graph means finding the configuration of the nodes that best satisfies the edges. This is generally achieved using iterative approaches that refine a current solution until convergence. Nowadays, iterative least-squares (ILS) algorithms such as Gauss–Newto...

In this paper, we present a sonar-based navigation system, designed to deploy a fleet of autonomous mobile platforms at a reasonable cost. In educational and hobbyist contexts, a large number of robots is required. By means of classical navigation approaches, every robot should be provided with accurate vision or range sensors. This limits the maxi...

The use of RGB-D cameras has become an affordable solution for robot mapping and navigation in contrast to expensive 2D laser range finders. Although these sensors provide richer information about the 3D environment, most successful mapping and navigation techniques for mobile robots have been developed considering a 2D planar environment. In this...

In this paper we propose a pose-landmark graph optimization back-end that supports maps consisting of points, lines or planes. Our back-end allows to represent both homogeneous ( ${point-point}$ , ${line-line}$ , ${plane-plane}$ ) and heterogeneous measurements ( ${point-on-line}$ , ${point-on-plane}$ , ${line-on-plane}$ ). Rather than tr...

The ability to maintain and continuously update geometric calibration parameters of a mobile platform is a key functionality for every robotic system. These parameters include the intrinsic kinematic parameters of the platform, the extrinsic parameters of the sensors mounted on it, and their time delays. In this letter, we present a unified pipelin...

Registering models is an essential building block of many robotic applications. In case of 3D data, the models to be aligned usually consist of point clouds. In this work we propose a formalism to represent in a uniform manner scenes consisting of high-level geometric primitives, including lines and planes. Additionally, we derive both an iterative...

Pose-Graph optimization is a crucial component of many modern SLAM systems. Most prominent state of the art systems address this problem by iterative non-linear least squares. Both number of iterations and convergence basin of these approaches depend on the error functions used to describe the problem. The smoother and more convex the error functio...

The use of RGB-D cameras has become an affordable solution for robot mapping and navigation in contrast to expensive 2D laser range finders. Although these sensors provide richer information about the 3D environment, most successful mapping and navigation techniques for mobile robots have been developed considering a 2D planar environment. In this...

The ability to build maps is a key functionality for the majority of mobile robots. A central ingredient to most mapping systems is the registration or alignment of the recorded sensor data. In this paper, we present a general methodology for photometric registration that can deal with multiple different cues. We provide examples for registering RG...

In this paper, we present a novel solution for real-time, Non-Linear Model Predictive Control (NMPC) exploiting a time-mesh refinement strategy. The proposed controller formulates the Optimal Control Problem (OCP) in terms of flat outputs over an adaptive lattice. In common approximated OCP solutions, the number of discretization points composing t...

The self-localization capability is a crucial component for Unmanned Ground Vehicles (UGV) in farming applications. Approaches based solely on visual information or on low-cost GPS are easily prone to fail in such scenarios. In this paper, we present a robust and accurate 3D global pose estimation framework, designed to take full advantage of heter...

The ability to build maps is a key functionality for the majority of mobile robots. A central ingredient to most mapping systems is the registration or alignment of the recorded sensor data. In this paper, we present a general methodology for photometric registration that can deal with multiple different cues. We provide examples for registering RG...

Point cloud registration is a fundamental building block of many robotic applications. In this paper we describe a system to solve the registration problem, that builds on top of our previous work (Serafin and Grisetti (2015)), and that represents an extension to the well known Iterative Closest Point (ICP) algorithm. Our approach combines recent a...

In this paper, we propose an approach for merging 3D maps represented as pose graphs of point clouds. Our method can effectively deal with typical distortions affecting SLAMgenerated maps. Traditional map merging techniques that use a single rigid body transformation to relate the reference frames of different maps. Instead, our approach achieves m...

Selective weeding is one of the key challenges in the field of agriculture robotics: in order to accomplish this task, a farm robot should be able to accurately detect plants and to distinguish them between crop and weeds. In this paper, we face this problem by proposing a novel and effective approach that aims to dramatically minimize the human in...

Matching 3D point clouds, a critical operation in map building and localization, is difficult with Velodyne-type sensors due to the sparse and non-uniform point clouds that they produce. Standard methods from dense 3D point clouds are generally not effective. In this paper, we describe a feature-based approach using Principal Components Analysis (P...

In this paper we present a novel on-line method to recursively align point clouds. By considering each point together with the local features of the surface (normal and curvature), our method takes advantage of the 3D structure around the points for the determination of the data association between two clouds. The algorithm relies on a least square...

Semantic mapping is the incremental process of "mapping" relevant information of the world (i.e., spatial information , temporal events, agents and actions) to a formal description supported by a reasoning engine. Current research focuses on learning the semantic of environments based on their spatial location, geometry and appearance. Many methods...

In this paper, we propose a quick and easy approach to estimate the undistortion function of RGBD sensors. Our method does not rely on the knowledge of the sensor model, on the use of a specific calibration pattern or on external SLAM systems to track the device position. We compute a nonparametric approximation of the undistortion function by appl...

Learning maps from sensor data has been addressed since more than two decades by Simultaneous Localization and Mapping (SLAM) systems. Modern state-of-the-art SLAM approaches exhibit excellent performances and are able to cope with environments having the scale of a city. Usually these methods are entailed for on-line operation, requiring the data...

Point cloud registration is an essential part for many robotics applications and this problem is usually addressed using some of the existing variants of the Iterative Closest Point (ICP) algorithm. In this paper we propose a novel variant of the ICP objective function which is minimized while searching for the registration. We show how this new fu...

Non-linear error minimization methods became widespread approaches for solving the simultaneous localization and mapping problem. If the initial guess is far away from the global minimum, converging to the correct solution and not to a local one can be challenging and sometimes even impossible. This paper presents an experimental analysis of dynami...

To carry out satisfactory object recognition in a short time. An object recognition method in accordance with an exemplary aspect of the present invention is an object recognition method for recognizing a target object by using a preliminarily-created object model. The object recognition method generates a range image of an observed scene, detects...

For autonomous robots, the ability to classify their local surroundings into traversable and non-traversable areas is crucial for navigation. In this paper, we address the problem of online traversability analysis for robots that are only equipped with a Kinect-style sensor. Our approach processes the depth data at 10 fps-25 fps on a standard noteb...

In this paper, we propose an approach to obtain highly accurate 3D models from range data. The key idea of our method is to jointly optimize the poses of the sensor and the positions of the surface points measured with a range scanning device. Our approach applies a physical model of the underlying range sensor. To solve the optimization task it em...

Recently Rao-Blackwellized particle lter s have been introduced as an effective means to solve the simultaneous localization and mapping (SLAM) problem. In such a particle lter each particle carries an individual map of the environment. Accordingly, a key question is how to reduce the number of particles. Additionally, the approach leaves open how...

In this article we describe an algorithm for constructing a compact repre-sentation of 3D laser range data. Our approach extracts a dictionary of local scans from the scene. The words of this dictionary are used to replace recurrent local 3D structures, which leads to a substantial compression of the entire point cloud. We optimize our model in ter...

Recently, there has been increased interest in the development of autonomous flying vehicles. However, as most of the proposed approaches are suitable for outdoor operation, only a few techniques have been designed for indoor environments, where the systems cannot rely on the Global Positioning System (GPS) and, therefore, have to use their exteroc...

A large number of applications use motion capture systems to track the location and the body posture of people. For instance, the movie industry captures actors to animate virtual characters that perform stunts. Today's tracking systems either operate with statically mounted cameras and thus can be used in confined areas only or rely on inertial se...

In graph-based SLAM, the pose graph encodes the poses of the robot during data acquisition as well as spatial constraints between them. The size of the pose graph has a substantial influence on the runtime and the memory requirements of a SLAM system, which hinders long-term mapping. In this paper, we address the problem of efficient information-th...

The calibration parameters of a mobile robot play a substantial role in navigation tasks. Often these parameters are subject to variations that depend either on environmental changes or on the wear of the devices. In this paper, we propose an approach to simultaneously estimate a map of the environment, the position of the on-board sensors of the r...

In this paper we describe an algorithm for learning highly accurate laser-based maps that treats the overall mapping problem as a joint optimization problem over robot poses and laser points. We assume that a laser range finder senses points sampled from a regular surface and we utilize an improved likelihood function that accounts for two phenomen...