PlaneSLAM: Plane-based LiDAR SLAM for Motion Planning in Structured 3D Environments

Abstract

LiDAR sensors are a powerful tool for robot simultaneous localization and mapping (SLAM) in unknown environments, but the raw point clouds they produce are dense, computationally expensive to store, and unsuited for direct use by downstream autonomy tasks, such as motion planning. For integration with motion planning, it is desirable for SLAM pipelines to generate lightweight geometric map representations. Such representations are also particularly wellsuited for man-made environments, which can often be viewed as a so-called "Manhattan world" built on a Cartesian grid. In this work we present a 3D LiDAR SLAM algorithm for Manhattan world environments which extracts planar features from point clouds to achieve lightweight, real-time localization and mapping. Our approach generates plane-based maps which occupy significantly less memory than their point cloud equivalents, and are suited towards fast collision checking for motion planning. By leveraging the Manhattan world assumption, we target extraction of orthogonal planes to generate maps which are more structured and organized than those of existing plane-based LiDAR SLAM approaches. We demonstrate our approach in the high-fidelity AirSim simulator and in realworld experiments with a ground rover equipped with a Velodyne LiDAR. For both cases, we are able to generate high quality maps and trajectory estimates at a rate matching the sensor rate of 10 Hz.

MR-Md:multi-Robot Mapping With Manhattan Descriptor

“…However, the application of this assumption remains limited. (Dai et al, 2022) used the Manhattan World assumption to construct maps based on navigation needs but found a slight improvement in mapping accuracy. Based on this, we propose a multi-robot collaborative mapping method named MR-MD that uses Manhattan descriptors (MD) to overcome these limitations and improve the perception accuracy of LiDAR SLAM in indoor environments.…”

Planar Constraint Assisted LiDAR SLAM Algorithm Based on Manhattan World Assumption

“…In the field of LiDAR SLAM, there are still few related studies. The authors of [19] used the MW assumption to extract orthogonal planes and generate plane-based maps, which are more convenient for subsequent path planning while occupying less memory. However, the algorithm is more focused on the real-time performance of the system rather than accuracy.…”