Geometry-aware Deep LiDAR Odometry

Learning-based ego-motion estimation approaches have recently drawn strong interest from researchers, mostly focusing on visual perception. A few learning-based approaches using LiDAR have been reported; however, they heavily rely on a supervised learning manner. Despite the meaningful performance of these approaches, supervised training requires ground-truth pose labels which is the bottleneck for real-world applications. Differing from these approaches, we focus on unsupervised learning for LiDAR odometry (LO) without trainable labels. Achieving trainable LO in an unsupervised manner, we introduce the uncertainty-aware loss with geometric confidence, thereby allowing the reliability of the proposed pipeline. Evaluation on the KITTI, Complex Urban, and Oxford RobotCar datasets demonstrate the prominent performance of the proposed method compared to conventional model-based methods. The proposed method shows a comparable result against SuMa (in KITTI), LeGO-LOAM (in Complex Urban) and Stereo-VO (in Oxford RobotCar).