PRBonn/rko_lio

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

What it solves

RKO-LIO provides a robust LiDAR-Inertial Odometry (LIO) system that allows robots to track their position and orientation in real-time without requiring complex, sensor-specific modeling. This reduces the friction typically associated with setting up odometry systems for different hardware platforms.

How it works

The system fuses data from a LiDAR sensor and an Inertial Measurement Unit (IMU) to estimate the robot's movement. It is designed to be platform-agnostic, meaning it can operate across various environments and hardware configurations with minimal configuration, provided the extrinsic transformations between the IMU and LiDAR are specified.

Who it’s for

It is designed for robotics developers and researchers who need a reliable, "it just works" odometry solution that can be integrated into ROS (Robot Operating System) or used as a standalone Python package.

Highlights

  • Sensor Agnostic: Works across multiple platforms and environments without needing specific sensor models.
  • Dual Interface: Available as both a Python package (via pip) and a ROS package (supporting Humble, Jazzy, Kilted, Lyrical, and Rolling).
  • Broad Data Support: Compatible with both ROS1 and ROS2 bags for data playback and testing.
  • Integrated Visualization: Supports high-performance visualization through the Rerun SDK.

Related

  • Project
    ouguangjun/Leg-KILO

    A real-time LiDAR-based SLAM system designed for high-dynamic platforms like legged robots, featuring a two-stage ESKF frontend and factor graph backend for robust mapping.

  • Project
    liangheming/FASTLIO2_ROS2

    A ROS2 adaptation of FASTLIO2 that adds loop closure, relocalization, and consistency map optimization for LiDAR-Inertial Odometry.

  • Project
    gaoxiang12/faster-lio

    A lightweight Lidar-inertial odometry system for high-speed pose tracking and point cloud mapping, offering 1.5-2x speed increase over FastLIO2.

  • Project
    superxslam/SuperOdom

    A lightweight LiDAR-inertial odometry and mapping system that provides robust pose estimation and degeneracy detection for robots using ROS 2.

  • Project
    93won/lightweight_vio

    A lightweight stereo visual-inertial odometry system that uses statistical uncertainty learning and sliding window optimization for real-time robot state estimation.