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Index repo for Kimera code
The main features of mit-spark/kimera are: Scene Understanding, Simultaneous Localization and Mapping, SLAM and Mapping, State Estimation and SLAM.
Open-source alternatives to mit-spark/kimera include: hkust-aerial-robotics/vins-fusion — VINS-Fusion is a multi-sensor fusion framework and visual-inertial odometry system. It integrates camera images,… tixiaoshan/lio-sam — LIO-SAM is a lidar inertial SLAM framework and tightly-coupled sensor fusion pipeline. It functions as a factor graph… introlab/rtabmap — This project is a comprehensive library and toolkit for simultaneous localization and mapping, designed to construct… ros-planning/navigation — This project is a framework for autonomous mobile robot navigation within the Robot Operating System ecosystem. It… edwardliuyc/staticmapping — Use LiDAR to map the static world. cvg/hierarchical-localization — This project is a 3D visual localization framework designed to determine a camera's exact position and orientation by…
VINS-Fusion is a multi-sensor fusion framework and visual-inertial odometry system. It integrates camera images, inertial measurement unit data, and global positioning signals through a non-linear optimization system to track the position and orientation of autonomous vehicles. The system includes a visual loop closure engine that utilizes a bag-of-words approach to recognize previously visited locations and correct trajectory drift. It further provides tools for online spatio-temporal calibration to determine the physical offset and time synchronization between cameras and inertial sensors d
LIO-SAM is a lidar inertial SLAM framework and tightly-coupled sensor fusion pipeline. It functions as a factor graph optimization engine that combines lidar scans and inertial measurement unit data to build 3D point cloud maps and estimate robot trajectories. The system integrates global position factors to align local coordinates with real-world data. It employs loop closure detection to identify previously visited locations, creating constraints in the optimization graph to correct accumulated global drift. The framework covers lidar inertial odometry, point cloud processing, and trajecto
This project is a comprehensive library and toolkit for simultaneous localization and mapping, designed to construct three-dimensional environment models while tracking device position. It functions as a robotics perception framework that processes data from RGB-D, stereo, and lidar sensors to enable autonomous navigation and spatial awareness. The system distinguishes itself through its focus on long-term mapping and global consistency. It employs a sophisticated loop-closure detection engine and graph-based pose optimization to identify previously visited locations and eliminate cumulative
This project is a framework for autonomous mobile robot navigation within the Robot Operating System ecosystem. It provides a suite of tools for calculating safe trajectories and movement commands, enabling mobile bases to reach specific destinations while avoiding obstacles in dynamic environments. The system utilizes a hierarchical planning approach that separates long-range path generation from short-range reactive obstacle avoidance. It maintains spatial awareness through a centralized coordinate tracking system and a grid-based representation that stores obstacle information and proximit