AI Agent Skills

A Robust Approach for LiDAR-Inertial Odometry Without Sensor-Specific Modelling

LiLi-OM is a tightly-coupled, keyframe-based LiDAR-inertial odometry and mapping system for both solid-state-LiDAR and conventional LiDARs.

A LiDAR odometry pipeline for wheeled mobile robots

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

Radar odometry code which removes motion distortion and Doppler effects from Navtech radar data.

Unsupervised radar odometry combining deep learning with classical state estimation

Official ROS drivers for Ouster sensors (OS0, OS1, OS2, OSDome)

The LiDAR segmenters library, for segmentation-based detection.

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Laser Odometry and Mapping (Loam) is a realtime method for state estimation and mapping using a 3D lidar.

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

Robust Odometry and Mapping for Multi-LiDAR Systems with Online Extrinsic Calibration

[RA-L 2024] In Defense of LiDAR-Only Odometry Using an Effective Continuous-Time Trajectory

This package provides localization in a pre-built map using ICP and odometry (or the IMU measurements).

Tightly-coupled Direct LiDAR-Inertial Odometry and Mapping Based on Cartographer3D.

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