DeLiO: Decoupled LiDAR Odometry
Most LiDAR odometry algorithms estimate the transformation between two consecutive frames by estimating the rotation and translation in an intervening fashion. In this paper, we propose our Decoupled LiDAR Odometry (DeLiO), which -- for the first time -- decouples the rotation estimation completely from the translation estimation. In particular, the rotation is estimated by extracting the surface normals from the input point clouds and tracking their characteristic pattern on a unit sphere. Using this rotation the point clouds are unrotated so that the underlying transformation is pure translation, which can be easily estimated using a line cloud approach. An evaluation is performed on the KITTI dataset and the results are compared against state-of-the-art algorithms.
Code (0)
등록된 구현이 없습니다.
Tasks
TranslationSimilar Papers 제목 키워드 기반
Pseudo-LiDAR for Visual Odometry
In the existing methods, LiDAR odometry shows superior performance, but visual odometry is still widely used for its price advantage. Conventionally, the task of visual odometry mainly rely on the input of continuous ima…
Stereo MatchingVisual OdometryOpen Set Dandelion Network for IoT Intrusion Detection
As IoT devices become widely, it is crucial to protect them from malicious intrusions. However, the data scarcity of IoT limits the applicability of traditional intrusion detection methods, which are highly data-dependen…
Domain AdaptationIntrusion DetectionTransfer LearningA Tightly Coupled LiDAR-IMU Odometry through Iterated Point-Level Undistortion
Scan undistortion is a key module for LiDAR odometry in high dynamic environment with high rotation and translation speed. The existing line of studies mostly focuses on one pass undistortion, which means undistortion fo…
TranslationPaGO-LOAM: Robust Ground-Optimized LiDAR Odometry
Numerous researchers have conducted studies to achieve fast and robust ground-optimized LiDAR odometry methods for terrestrial mobile platforms. In particular, ground-optimized LiDAR odometry usually employs ground segme…
SegmentationVisual-LiDAR Odometry and Mapping with Monocular Scale Correction and Visual Bootstrapping
This paper presents a novel visual-LiDAR odometry and mapping method with low-drift characteristics. The proposed method is based on two popular approaches, ORB-SLAM and A-LOAM, with monocular scale correction and visual…
Motion CompensationVisual Odometry