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LIO-SAM: Tightly-coupled Lidar Inertial Odometry via Smoothing and Mapping

  • lidar-odometry
  • lidar-slam
  • loam-velodyne
  • lidar-inertial

pySLAM is a hybrid Python/C++ Visual SLAM pipeline supporting monocular, stereo, and RGB-D cameras. It provides a broad set of modern local and global feature extractors, multiple loop-closure strategies, a volumetric reconstruction module, integrated depth-prediction models, and semantic segmentation capabilities for enhanced scene understanding.

  • 3d-reconstruction
  • depth-prediction
  • feature-matching
  • global-features

An open source platform for visual-inertial navigation research.

  • visual-inertial-odometry
  • slam
  • msckf
  • sensor-calibration

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

  • velodyne
  • ros
  • loam-velodyne
  • lidar

Implementation of Tightly Coupled 3D Lidar Inertial Odometry and Mapping (LIO-mapping)

  • sensor-fusion
  • slam
  • mapping
  • icra2019

A lightweight, accurate and robust monocular visual inertial odometry based on Multi-State Constraint Kalman Filter.

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

A ROS wrapper for libviso2, a library for visual odometry