PARTICLE FILTER BASED DETECTION OF DYNAMIC OBJECTS ON AN ACCUMULATED OCCUPANCY MAP
Abstract
The paper considers the problem of detecting dynamic obstacles on the accumulated occupancy
map generated by the computer vision system of a mobile robot. The purpose of this research is to
improve the quality of the obstacle detection algorithm by adding a particle filter to find moving objects
from the map data. In the paper, the problem of correct accumulation of data in the occupancy
map and reducing the delay in updating the map cells in which the object moves is solved. The modification
of the particle filter presented in the paper is able to work correctly with dynamic obstacles
in a wide range of speeds; it is resistant to outliers caused by random generation of the initial particles
velocities, and is workable under real conditions in real time in an environment with a lot of
moving objects. A heuristic has been created that reduces the number of misclassifications in occluded
areas. It is shown that the algorithm for detecting dynamic objects in the map is invariant to the
type of sensors used in the vision system, and an implementation combined with an accumulated
occupancy map is described. The algorithm is implemented and tested on board an autonomous mobile
robot, as well as on an open dataset. The article also provides a comparison with other approaches
of dynamic obstacles detection, as well as calculated performance metrics for all analyzed
methods for computers based on the GPU Nvidia RTX 3070 and Jetson AGX Xavier. Promising directions
for further research to improve the presented algorithm are formulated.
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