6D SLAM—3D mapping outdoor environments

Citations Over TimeTop 1% of 2007 papers

Abstract

Abstract 6D SLAM (simultaneous localization and mapping) or 6D concurrent localization and mapping of mobile robots considers six dimensions for the robot pose, namely, the x , y , and z coordinates and the roll, yaw, and pitch angles. Robot motion and localization on natural surfaces, e.g., driving outdoor with a mobile robot, must regard these degrees of freedom. This paper presents a robotic mapping method based on locally consistent 3D laser range scans. Iterative Closest Point scan matching, combined with a heuristic for closed loop detection and a global relaxation method, results in a highly precise mapping system. A new strategy for fast data association, cached k d‐tree search, leads to feasible computing times. With no ground‐truth data available for outdoor environments, point relations in maps are compared to numerical relations in uncalibrated aerial images in order to assess the metric validity of the resulting 3D maps. © 2007 Wiley Periodicals, Inc.

Related Papers

• → Registration with the Point Cloud Library: A Modular Framework for Aligning in 3-D(2015)294 cited
• → Point cloud registration based on improved ICP algorithm(2018)32 cited
• → FAST ICP-SLAM for a bi-steerable mobile robot in large environments(2015)7 cited
• → Self-localization Using Point Cloud Matching at the Object Level in Outdoor Environment(2019)3 cited
• → Research on 3D LiDAR Point Cloud Matching Localization Method(2022)2 cited