Popular GitHub Repositories for Inertial Odometry

An unsupervised learning framework for depth and ego-motion estimation from monocular videos

Roadmap to become a Visual-SLAM developer in 2023

Visual SLAM/odometry package based on NVIDIA-accelerated cuVSLAM

LeGO-LOAM, LIO-SAM, LVI-SAM, FAST-LIO2, Faster-LIO, VoxelMap, R3LIVE, Point-LIO, KISS-ICP, DLO, DLIO, Ada-LIO, PV-LIO, SLAMesh, ImMesh, FAST-LIO-MULTI, M-LOAM, LOCUS, SLICT, MA-LIO, CT-ICP, GenZ-ICP, iG-LIO, SR-LIO application and comparison on Gazebo and real-world datasets. Installation and config files are provided.

[ICRA 2025] MAC-VO: Metrics-aware Covariance for Learning-based Stereo Visual Odometry

GenZ-ICP: SOTA robust LiDAR odometry (IEEE RA-L 2025)

Unsupervised Learning of Monocular Depth Estimation and Visual Odometry with Deep Feature Reconstruction

Semantic LIDAR odometry and mapping for cylinderical objects (e.g. trees in forests)

Learning Depth from Monocular Videos using Direct Methods, CVPR 2018

Tensorflow and PyTorch implementation of Unsupervised Depth Completion from Visual Inertial Odometry (in RA-L January 2020 & ICRA 2020)

Code for "Photometric Visual-Inertial Navigation with Uncertainty-Aware Ensembles" in TRO 2022

CAE-LO: LiDAR Odometry Leveraging Fully Unsupervised Convolutional Auto-Encoder for Interest Point Detection and Feature Description

Deep Learning-Based Gait Recognition Using Smartphones in the Wild

首个基于LiDAR里程计的六自由度样条递归运动估计器

Implementation of the paper "Transformer-based model for monocular visual odometry: a video understanding approach".

This is the official Pytorch implementation of the IROS 2024 paper Deep Visual Odometry with Events and Frames using Recurrent Asynchronous and Massively Parallel (RAMP) networks for Visual Odometry (VO).

Code for T-ITS paper "Unsupervised Learning of Depth, Optical Flow and Pose with Occlusion from 3D Geometry" and for ICRA paper "Unsupervised Learning of Monocular Depth and Ego-Motion Using Multiple Masks".

[ECCV 2022]JPerceiver: Joint Perception Network for Depth, Pose and Layout Estimation in Driving Scenes

A fully-annotated, open-design dataset of autonomous and piloted high-speed flight

StereoVision-SLAM is a real-time visual stereo SLAM (Simultaneous Localization and Mapping)