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LeGO-LOAM: Lightweight and Ground-Optimized Lidar Odometry and Mapping on Variable Terrain

LeGO-LOAM introduces a cutting-edge lidar odometry and mapping framework designed to deliver real-time, accurate 6-DOF pose estimation for ground vehicles, optimized for challenging, variable terrain environments. It significantly reduces computational overhead while maintaining high accuracy, making it ideal for embedded systems.

Key Highlights

  • Ground-Optimized Approach – Segments lidar point clouds by leveraging ground plane information, filtering out unreliable features from grass, tree leaves, and other noisy objects.
  • Two-Step Levenberg-Marquardt Optimization – Separates translation and rotation estimation into two steps, reducing computation time by 35-48% while maintaining accuracy.
  • Real-Time Pose Estimation – Achieves real-time performance on embedded systems like the Jetson TX2, with a runtime reduction of over 60% compared to traditional LOAM.
  • Reduced Computational Overhead – Significantly decreases the number of features to be processed, improving processing speed by up to 72%.
  • Improved Accuracy in Complex Environments – Successfully performs pose estimation and mapping in noisy outdoor environments like forests and urban settings with minimal drift.
  • SLAM Integration – Integrates with pose-graph SLAM to eliminate drift over longer durations, ensuring high accuracy even during extended operations.
  • Platform Agnostic – Validated on different hardware setups, including Velodyne VLP-16 and HDL-64E, showcasing its adaptability across various platforms.
  • Efficient for Embedded Systems – Leverages low-power embedded devices, ensuring real-time performance even with limited resources.

LeGO-LOAM represents a significant step forward in lightweight, real-time lidar mapping, making it highly suitable for unmanned ground vehicles (UGVs) navigating variable terrains.

Paper Resources

Paper: https://ieeexplore.ieee.org/document/8594299
Github: https://github.com/RobustFieldAutonomyLab/LeGO-LOAM
Video Demo:

  1. https://www.youtube.com/watch?v=O3tz_ftHV48
  2. https://youtu.be/3bkjse1keSA?feature=shared
  1. LiDAR SLAM: LOAM and LeGO-LOAM: https://learnopencv.com/lidar-slam-with-ros2/
  2. Monocular SLAM in Python: https://learnopencv.com/monocular-slam-in-python/
  3. MASt3R SLAM: https://learnopencv.com/mast3r-slam-realtime-dense-slam-explained/

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