Lidar Odometry
Showing 11 of 11 projects
A real-time, tightly-coupled lidar-inertial odometry package for robust robot localization and mapping.
A computationally efficient and robust LiDAR-inertial odometry (LIO) package using a tightly-coupled iterated Kalman filter.
A lightweight, ground-optimized lidar odometry and mapping system for ROS-compatible unmanned ground vehicles.
A clean, simplified implementation of the LOAM algorithm for real-time LiDAR odometry and mapping using Eigen and Ceres Solver.
A robust LiDAR odometry pipeline that works out-of-the-box without parameter tuning for accurate robot localization.
A realtime LiDAR odometry and mapping (LOAM) method for state estimation and mapping using 3D lidar sensors like Velodyne VLP16.
A tightly coupled 3D LiDAR-inertial odometry and mapping system for real-time robot localization and mapping.
A modular C++ and ROS 2 framework for building configurable LiDAR odometry and SLAM pipelines.
A long-term autonomous driving dataset from Europe with multi-sensor data (GPS-RTK, LiDAR, cameras, IMU) for localization and mapping research.
A recursive B-spline-based state estimation framework for 6-DoF LiDAR odometry, supporting LiDAR-only, LiDAR-inertial, and multi-LiDAR configurations.
C++ libraries for multi primitive-to-primitive ICP algorithms and flexible point cloud processing pipelines.
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