RO-LOAM: 3D Reference Object-based Trajectory and Map Optimization in LiDAR Odometry and Mapping

Abstract

We propose an extension to the LiDAR Odometry and Mapping framework (LOAM) that enables reference object-based trajectory and map optimization. Our approach assumes that the location and geometry of a large reference object are known, e.g., as a CAD model from Building Information Modeling (BIM) or a previously captured dense point cloud model. We do not expect the reference object to be present in every LiDAR scan. Our approach uses the poses of the LOAM algorithm as an initial guess to refine them with scan-to-model alignment. To evaluate if the alignment was accurate, an EKF-based motion prior filtering step is employed. Subsequently, the past trajectory is optimized by adding the model-aligned pose as a pose graph constraint and the map of the LOAM algorithm is corrected to improve future localization and mapping. We evaluate our approach with data captured in a visual airplane inspection scenario inside an aircraft hangar. A 3D LiDAR sensor is mounted via a gimbal on an Unmanned Aerial Vehicle (UAV) and is continuously actuated. We compare the localization accuracy of the LOAM and R-LOAM algorithms when enabling or disabling our proposed reference object-based trajectory and map optimization extension. For three recorded datasets, enabling the proposed extension yields a reduction in Absolute Pose Error compared to conventional LOAM and R-LOAM, while being able to run online. This reduces drift and improves map quality.