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[Advances in Intelligent Systems and Computing] Intelligent Autonomous Systems 15 Volume 867 (Proceedings of the 15th International Conference IAS-15) || Stereo Vision-Based Optimal Path Planning with Stochastic Maps for Mobile Robot Navigation
摘要： This paper addresses the problem of stereo vision-based environment mapping and optimal path planning for an autonomous mobile robot by using the methodology of getting 3D point cloud from disparity images, its transformation to 2D stochastic navigation map with occupancy grid cell values assigned from the set {obstacle, unoccupied, occupied}. We re-examined and extended this methodology with a combination of A-Star with binary heap algorithms for obstacle avoidance and indoor navigation of the Innopolis autonomous mobile robot with ZED stereo camera.
关键词： Mobile robot navigation,Occupancy grid,Disparity image,Stochastic map,Optimal path planning,Stereo vision
更新于2025-09-23 15:22:29
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[IEEE 2018 IEEE Conference on Control Technology and Applications (CCTA) - Copenhagen (2018.8.21-2018.8.24)] 2018 IEEE Conference on Control Technology and Applications (CCTA) - Improving Occupancy Grid Mapping via Dithering for a Mobile Robot Equipped with Solid-State LiDAR Sensors
摘要： Occupancy grid maps are by far the most used spatial representation of the environment for robot navigation. This paper proposes a simple and effective way to improve the occupancy grid accuracy by superimposing a small oscillation to the robot motion when a predefined path is given. The method is especially suited for range sensors with long range capabilities but poor angular resolution. The innovative solid state LiDAR technology is an example of such sensor configuration and is used in this work for the experimental evaluation of the presented dithering technique. Experimental results quantitatively demonstrated that the proposed oscillating motion is effective especially in speeding up the detection of corridor like clearances in the environment.
关键词： dithering technique,solid state LiDAR,robot navigation,Occupancy grid maps
更新于2025-09-04 15:30:14
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[IEEE 2018 21st International Conference on Information Fusion (FUSION 2018) - Cambridge (2018.7.10-2018.7.13)] 2018 21st International Conference on Information Fusion (FUSION) - Motion State Classification for Automotive LIDAR Based on Evidential Grid Maps and Transferable Belief Model
摘要： Point clouds generated by Lidar sensors provide detailed information about the geometry of the environment. Yet they lack semantic information, which is paramount for the choice of choosing the appropriate modelling, e.g. within a tracking system. This contribution tries to provide semantic information in the form of stationary and dynamic classification by applying a transferable belief model. In particular, we build an occupancy grid representation of the environment and correct it with a tailored transferable belief model that accounts for inconsistencies as well as non-local features. Based on these results we define a classifier that distinguishes between stationary and dynamic cells. The presented algorithm is evaluated qualitatively on real-world Valeo Scala LIDAR data and quantitatively based on an IPG Carmaker simulation.
关键词： motion classification,transferable belief model,semantic information,LIDAR,occupancy grid
更新于2025-09-04 15:30:14