Abstract
Robots require a certain set of skills to perceive and analyse the environment and act accordingly. For tracked mobile robots getting good odometry data from sensory information is a challenging key prerequisite to perform in an unstructured dynamic environment, thus an essential issue in the tracked mobile robotics domain. In this article, we construct a ROS-based tracked mobile robot system taking the Jaguar V4 mobile robot as the base platform. On which several visual odometry solutions based on different cameras and methods (Intel RealSense T265, Zed camera, RTAB-Map RGBD) are integrated and benchmark comparison is performed. Analysis of new challenges faced by different methods while applied on a tracked vehicle as well as recommendations and conclusions are presented. Intel RealSense T265 solution proved to perform well in uncertain conditions which involves bounded vibrations and low lighting conditions with low latency, which result in good map generation. Further evaluations with a path planning algorithm and Intel RealSense T265 were conducted to test the effect of the robot’s motion profiles on odometry data accuracy.
Highlights
Nowadays robots are being used in various applications within health care [1], agriculture [2], food preparation [3], industrial machinery [4], manufacturing [5], military [6] or in a collaboration with humans [7] and other sectors
The maximum acceleration was limited between 0.3 m/s2 and 0.7 m/s2 as the V-Simultaneous Localisation and Mapping (SLAM) algorithm running on Intel RealSense T265 gave poor linear vector data when the accelerations were below 0.3 m/s2 and caused SLAM errors due to inbound vibrations when accelerations were above 0.7 m/s2
A robot operating system (ROS)-based autonomous navigation system for a tracked mobile robot was proposed, on which we have integrated and benchmarked the performance of different visual-inertial odometry methods providing a comparative analysis of odometry data and generated maps
Summary
Nowadays robots are being used in various applications within health care [1], agriculture [2], food preparation [3], industrial machinery [4], manufacturing [5], military [6] or in a collaboration with humans [7] and other sectors. Mobile robotics has received a great deal of attention in the past decades, for example, the market for professional service robots grew in 2020 by 12 % [10]. The demand for professional cleaning robots grew by 92 % in terms of units sold [10]. In most of these new applications, the robot needs to be autonomous and to be able to reason about the environment and take decisions . Most of the environments nowadays where these robots are deployed can be highly unstructured, partially observable, and potentially inaccessible/human un- friendly
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