We present a novel deep convolutional network pipeline, LO-Net, for real-time lidar odometry estimation. Unlike most existing lidar odometry (LO) estimations that go through individually designed feature selection, feature matching, and pose estimation pipeline, LO-Net can be trained in an end-to-end manner. With a new mask-weighted geometric constraint loss, LO-Net can effectively learn feature representation for LO estimation, and can implicitly exploit the sequential dependencies and dynamics in the data. We also design a scan-to-map module, which uses the geometric and semantic information learned in LO-Net, to improve the estimation accuracy. Experiments on benchmark datasets demonstrate that LO-Net outperforms existing learning based approaches and has similar accuracy with the state-of-the-art geometry-based approach, LOAM.
@cite_0: In this paper we present a novel on-line method to recursively align point clouds. By considering each point together with the local features of the surface (normal and curvature), our method takes advantage of the 3D structure around the points for the determination of the data association between two clouds. The algorithm relies on a least squares formulation of the alignment problem, that minimizes an error metric depending on these surface characteristics. We named the approach Normal Iterative Closest Point (NICP in short). Extensive experiments on publicly available benchmark data show that NICP outperforms other state-of-the-art approaches.
@cite_22: Robotics: Science and Systems III spans a wide spectrum of robotics, bringing together researchers working on the foundations of robotics, robotics applications, and analysis of robotics systems. This volume presents the proceedings of the third annual Robotics: Science and Systems conference, held in June 2007 at Georgia Tech. Papers report state-of-the-art research on topics as diverse as Legged Robotics, Reconfigurable Robots, Biomimetic Robots, Manipulation, Humanoid Robotics, Telerobotics, Haptics, Motion Planning, Collision Avoidance, Robot Vision and Perception, Bayesian Techniques, Machine Learning, Mobile Robots, and Multi-robot systems. This conference reflects not only the tremendous growth of robotics as a discipline but also the desire in the robotics community for a flagship event at which the best of the research in the field can be presented.
By combining point-to-point ICP and point-to-plane ICP, Segal al @cite_22 introduce a plane-to-plane strategy called Generalized ICP (GICP). The covariance matrices of the local surfaces is used to match corresponding point clouds. Their results show better accuracy over original ICP. The Normal Iterative Closest Point (NICP) @cite_0 takes into account the normal and curvature of each local surface point. Experiments show that NICP can offer better robustness and performance than the GICP.