4,533 research outputs found
Topological Navigation of Simulated Robots using Occupancy Grid
Formerly I presented a metric navigation method in the Webots mobile robot
simulator. The navigating Khepera-like robot builds an occupancy grid of the
environment and explores the square-shaped room around with a value iteration
algorithm. Now I created a topological navigation procedure based on the
occupancy grid process. The extension by a skeletonization algorithm results a
graph of important places and the connecting routes among them. I also show the
significant time profit gained during the process
A tesselated probabilistic representation for spatial robot perception and navigation
The ability to recover robust spatial descriptions from sensory information and to efficiently utilize these descriptions in appropriate planning and problem-solving activities are crucial requirements for the development of more powerful robotic systems. Traditional approaches to sensor interpretation, with their emphasis on geometric models, are of limited use for autonomous mobile robots operating in and exploring unknown and unstructured environments. Here, researchers present a new approach to robot perception that addresses such scenarios using a probabilistic tesselated representation of spatial information called the Occupancy Grid. The Occupancy Grid is a multi-dimensional random field that maintains stochastic estimates of the occupancy state of each cell in the grid. The cell estimates are obtained by interpreting incoming range readings using probabilistic models that capture the uncertainty in the spatial information provided by the sensor. A Bayesian estimation procedure allows the incremental updating of the map using readings taken from several sensors over multiple points of view. An overview of the Occupancy Grid framework is given, and its application to a number of problems in mobile robot mapping and navigation are illustrated. It is argued that a number of robotic problem-solving activities can be performed directly on the Occupancy Grid representation. Some parallels are drawn between operations on Occupancy Grids and related image processing operations
Radar-based Dynamic Occupancy Grid Mapping and Object Detection
Environment modeling utilizing sensor data fusion and object tracking is
crucial for safe automated driving. In recent years, the classical occupancy
grid map approach, which assumes a static environment, has been extended to
dynamic occupancy grid maps, which maintain the possibility of a low-level data
fusion while also estimating the position and velocity distribution of the
dynamic local environment. This paper presents the further development of a
previous approach. To the best of the author's knowledge, there is no
publication about dynamic occupancy grid mapping with subsequent analysis based
only on radar data. Therefore in this work, the data of multiple radar sensors
are fused, and a grid-based object tracking and mapping method is applied.
Subsequently, the clustering of dynamic areas provides high-level object
information. For comparison, also a lidar-based method is developed. The
approach is evaluated qualitatively and quantitatively with real-world data
from a moving vehicle in urban environments. The evaluation illustrates the
advantages of the radar-based dynamic occupancy grid map, considering different
comparison metrics.Comment: Accepted to be published as part of the 23rd IEEE International
Conference on Intelligent Transportation Systems (ITSC), Rhodes, Greece,
September 20-23, 202
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