Our paper is currently under review, and the code of ROG-Map will be released as our work is accepted. With MARSIM, you can test your own motion planning algorithms based on ROG-Map.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.However, when used in the real domestic scene, grid maps are lack of semantic information for end users to specify navigation tasks. Occupancy grid maps are sufficient for mobile robots to complete point-to-point navigation tasks in 2-D small-scale environments. The ROG-Map is an occupancy grid map (OGM), and all methods based on OGM can be seamlessly implemented on ROG-Map, including: This article proposes a semantic grid mapping method for domestic robot navigation. An Efficient Robocentric Occupancy Grid Map for Large-scene and High-resolution LiDAR-based Motion Planning},Īuthor=,
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