408 research outputs found
An effective 6DoF motion model for 3D-6DoF Monte Carlo Localization
This paper deals with the probabilistic 6DoF motion model of a wheeled road vehicle. It allows to correctly model the error introduced by dead reckoning. Furthermore, to stress the importance of an appropriate motion model, i.e., that different models are not equally good, we show that another model, which was previously developed, does not allow a correct representation of the uncertainty, therefore misguiding 3D-6DoF Monte Carlo Localization. We also present some ïŹeld experiments to demonstrate that our model allow a consistent determination of the 6DoF vehicle pose
Open Source Robot Localization for Non-Planar Environments
The operational environments in which a mobile robot executes its missions
often exhibit non-flat terrain characteristics, encompassing outdoor and indoor
settings featuring ramps and slopes. In such scenarios, the conventional
methodologies employed for localization encounter novel challenges and
limitations. This study delineates a localization framework incorporating
ground elevation and inclination considerations, deviating from traditional 2D
localization paradigms that may falter in such contexts. In our proposed
approach, the map encompasses elevation and spatial occupancy information,
employing Gridmaps and Octomaps. At the same time, the perception model is
designed to accommodate the robot's inclined orientation and the potential
presence of ground as an obstacle, besides usual structural and dynamic
obstacles. We have developed and rigorously validated our approach within Nav2,
and esteemed open-source framework renowned for robot navigation. Our findings
demonstrate that our methodology represents a viable and effective alternative
for mobile robots operating in challenging outdoor environments or intrincate
terrains
Editorial: Special issue on ground robots operating in dynamic, unstructured and large-scale outdoor environments
Real-world outdoor applications of ground robots have, to date, been limited primarily to remote inspection of suspected explosive devices and, with less success, to the broader domain of remote survey and inspection in hazardous environments. Such robots have almost exclusively been tele-operated. Also notable as examples of outdoor ground robots are the planetary rovers, currently deployed with great success on the surface of Mars. But with the rapid development of autonomous (driverless) cars, and the emergence of robotic vehicles in agriculture, it is likely that there will be significant growth in both the numbers and scope of commercial ground robots in outdoor environments in the near future.For this special issue we called for papers that present land robot systems deployed in the field in similar realistic challenges. We sought papers that focus on any aspect of robotic systems, from vehicle design to the overall system architecture and control, via terrain mapping, localization, mission planning and execution â with an emphasis on systems that fulfil a specific real world task. We specified that robot or system innovations must be supported by extensive field results. Also that field tests must be under realistic and challenging conditions with respect to the terrain type, the scenario to be achieved, and/or the conditions within which the scenarios must be achieved
Towards 6D MCL for LiDARs in 3D TSDF Maps on Embedded Systems with GPUs
Monte Carlo Localization is a widely used approach in the field of mobile
robotics. While this problem has been well studied in the 2D case, global
localization in 3D maps with six degrees of freedom has so far been too
computationally demanding. Hence, no mobile robot system has yet been presented
in literature that is able to solve it in real-time. The computationally most
intensive step is the evaluation of the sensor model, but it also offers high
parallelization potential. This work investigates the massive parallelization
of the evaluation of particles in truncated signed distance fields for
three-dimensional laser scanners on embedded GPUs. The implementation on the
GPU is 30 times as fast and more than 50 times more energy efficient compared
to a CPU implementation
Mobile robot localization failure recovery
Mobile robot localization is one of the most important problems in robotics. Localization is the process of a robot finding out its location given a map of its environment. A number of successful localization solutions have been proposed, among them the well-known and popular Monte Carlo localization method, which is based on particle filters. This thesis proposes a localization approach based on particle filters, using a different way of initializing and resampling of the particles, that reduces the cost of localization. Ultrasonic and light sensors are used in order to perform the experiments. Monte Carlo Localization may fail to localize the robot properly because of the premature convergence of the particles. Using more number of particles increases the computational cost of localization process. Experimental results show that, applying the proposed method robot can successfully localize itself using less number of particles; therefore the cost of localization is decreased
LiDAR-Based Place Recognition For Autonomous Driving: A Survey
LiDAR-based place recognition (LPR) plays a pivotal role in autonomous
driving, which assists Simultaneous Localization and Mapping (SLAM) systems in
reducing accumulated errors and achieving reliable localization. However,
existing reviews predominantly concentrate on visual place recognition (VPR)
methods. Despite the recent remarkable progress in LPR, to the best of our
knowledge, there is no dedicated systematic review in this area. This paper
bridges the gap by providing a comprehensive review of place recognition
methods employing LiDAR sensors, thus facilitating and encouraging further
research. We commence by delving into the problem formulation of place
recognition, exploring existing challenges, and describing relations to
previous surveys. Subsequently, we conduct an in-depth review of related
research, which offers detailed classifications, strengths and weaknesses, and
architectures. Finally, we summarize existing datasets, commonly used
evaluation metrics, and comprehensive evaluation results from various methods
on public datasets. This paper can serve as a valuable tutorial for newcomers
entering the field of place recognition and for researchers interested in
long-term robot localization. We pledge to maintain an up-to-date project on
our website https://github.com/ShiPC-AI/LPR-Survey.Comment: 26 pages,13 figures, 5 table
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