There are 23 repositories under odometry topic.
Python package for the evaluation of odometry and SLAM
LeGO-LOAM: Lightweight and Ground-Optimized Lidar Odometry and Mapping on Variable Terrain
loam code noted in Chinese(loam中文注解版)
X Inertial-aided Visual Odometry
[IEEE RA-L & ICRA'22] A lightweight and computationally-efficient frontend LiDAR odometry solution with consistent and accurate localization.
[IEEE ICRA'23] A new lightweight LiDAR-inertial odometry algorithm with a novel coarse-to-fine approach in constructing continuous-time trajectories for precise motion correction.
Full-python LiDAR SLAM using ICP and Scan Context
Robust LiDAR SLAM with a versatile plug-and-play loop closing and pose-graph optimization.
😎 A curated list of awesome mobile robots study resources based on ROS (including SLAM, odometry and navigation, manipulation)
Estimation of 2D odometry based on planar laser scans. Useful for mobile robots with innacurate base odometry. For full description of the algorithm, please refer to: Planar Odometry from a Radial Laser Scanner. A Range Flow-based Approach. ICRA 2016 Available at: http://mapir.isa.uma.es/mapirwebsite/index.php/mapir-downloads/papers/217
A bunch of state estimation algorithms
This is the offical codes for the methods described in the "Feature-metric Loss for Self-supervised Learning of Depth and Egomotion".
T-LOAM: Truncated Least Squares Lidar-only Odometry and Mapping in Real-Time
Semantic LIDAR odometry and mapping for cylinderical objects (e.g. trees in forests)
A SLAM implementation combining FAST-LIO2 with pose graph optimization and loop closing based on Quatro and Nano-GICP
MATLAB Implementation of Visual Odometry using SOFT algorithm
ORORA: Outlier-robust radar odometry, which is accepted in ICRA'23
Soccer playing robot representing Canada from University of Toronto
LiDAR degeneracy dataset for LiDAR-radar-inertial fusion methods.
ROS Hexapod stack with functioning 2D and 3D mapping.
Efficient and accurate spinning FMCW 2D radar odometry in C++ / ROS.
This contains the code(in development) for monocular visual odometry of a quadrotor. The visual data from the monocular camera is fused with onboard IMU to develop indoor control and navigation algorithms.
Multi-LiDAR version of FAST-LIO2: A computationally efficient and robust LiDAR-inertial odometry (LIO) package
Tightly coupled lidar-visual-inertial slam using the fuse framework
SFUISE is a novel continuous-time UWB-inertial state estimation framework based on sliding-window spline fusion.