Open-source alternatives to Kimera

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

Official implementation for Learning Multi-View Aggregation In the Wild for Large-Scale 3D Semantic Segmentation (CVPR'22 Best Paper Finalist 🎉) If you ❤️ or simply use this project, don't forget to give the repository a ⭐, it means a lot to us !

Use LiDAR to map the static world

A Modular and Multi-Modal Mapping Framework

Habitat-Lab

Habitat-sim is a high-performance 3D simulation platform designed for training and benchmarking embodied AI agents within photorealistic indoor and outdoor environments. It serves as a simulator for AI and robotics, providing a system for generating synthetic data and simulating physical interactions. The project is distinguished by a native C++ core that enables high-throughput simulation and a rendering pipeline using physically based rendering and baked global illumination. It features a navigation system based on pre-computed navigation meshes to ensure collision-free traversal and a rigi

Properties of dilated convolution are discussed in our ICLR 2016 conference paper. This repository contains the network definitions and the trained models. You can use this code together with vanilla Caffe to segment images using the pre-trained models. If you want to train the models yourself,…

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

Cartographer is a software library and spatial localization engine for simultaneous localization and mapping. It provides a framework for calculating the precise position and orientation of a device while concurrently generating real-time 2D and 3D representations of its environment using lidar-based data. The system implements a real-time mapping approach that uses live sensor streams to track device heading and position. It utilizes a submap-based mapping strategy to divide environments into local maps that are aligned into a global map. The project covers a range of SLAM capabilities, inc

Advanced implementation of LOAM

This repository maintains the implementation of "Event-based Stereo Visual Odometry".

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

Direct Sparse Odometry

This repository contains pre-trained models and evaluation code for the project 'Single Image 3D Interpreter Network' (ECCV 2016).

DH3D: Deep Hierarchical 3D Descriptors for Robust Large-Scale 6DOF Relocalization

pyslam is a framework for Simultaneous Localization and Mapping that combines Python flexibility with C++ performance. It is a sparse SLAM implementation designed to map environment geometry and track device location by processing image frames into 3D points. The project features a bridge for exposing high-performance C++ classes to Python scripts using zero-copy memory sharing. This integration allows for switching between a scripting interface for rapid prototyping and a compiled core for execution speed. The system includes a spatial map optimizer to refine 3D point and camera pose estima

Atlas: End-to-End 3D Scene Reconstruction from Posed Images

Paper Accepted to CVPR 2022.

Paper

Paper

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

Free-form Description-guided 3D Visual Graph Networks for Object Grounding in Point Cloud

A LiDAR odometry pipeline that just works