![]() Our paper is currently under review, and the code of ROG-Map will be released as our work is accepted. Existing occupancy grid mapping algorithms decompose the high-dimensional map-ping problem into a collection of one-dimensional problems, where the occupancy of each grid cell is estimated independently. With MARSIM, you can test your own motion planning algorithms based on ROG-Map. This article describes a new algorithm for acquiring occupancy grid maps with mobile robots.Building a robocentric occupancy grid map directly using FAST-LIO as input.Run with FAST-LIO: A computationally efficient and robust LiDAR-inertial odometry (LIO) package When the code is released, you can test it with A novel incremental inflation method significantly decreases the computation time of obstacle inflation.Using a zero-copy map sliding strategy, ROG-Map maintains only a local map near the robot, enabling it to handle large-scale scene missions in unbounded environments.1.2 What are the differences compared to existing methods? We will provide numerous examples to help you apply ROG-Map to your own projects. Point collision check and line segment collision check. ![]() ![]() Frontier generation for autonomous exploration.The ROG-Map is an occupancy grid map (OGM), and all methods based on OGM can be seamlessly implemented on ROG-Map, including: An Efficient Robocentric Occupancy Grid Map for Large-scene and High-resolution LiDAR-based Motion Planning},Īuthor=,
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