MoLO: Drift‐free lidar odometry using a 3D model

Abstract

LiDAR odometry enables localising vehicles and robots in the environments where global navigation satellite systems (GNSS) are not available. An inherent limitation of LiDAR odometry is the accumulation of local motion estimation errors. Current approaches heavily rely on loop closure to optimise the estimated sensor poses and to eliminate the drift of the estimated trajectory. Consequently, these systems cannot perform real‐time localization and are therefore not practical for a navigation task. This paper presents MoLO, a novel model‐based LiDAR odometry approach to achieve real‐time and drift‐free localization using a 3D model of the environment containing planar surfaces, namely the structural elements of buildings. The proposed approach uses a 3D model of the environment to initialise the LiDAR pose and includes a scan‐to‐scan registration to estimate the pose for consecutive LiDAR scans. Re‐registering LiDAR scans to the 3D model at a certain frequency provides the global sensor pose and eliminates the drift of the trajectory. Pose graphs are built frequently to acquire a smooth and accurate trajectory. A geometry‐based method and a learning‐based method to register LiDAR scans with the 3D model are tested and compared. Experimental results show that MoLO can eliminate drift and achieve real‐time localization while providing an accuracy equivalent to loop closure optimization.