Cartographer is a system that provides real-time simultaneous localization and mapping (SLAM) in 2D and 3D across multiple platforms and sensor configurations.

GLIM: versatile and extensible point cloud-based 3D localization and mapping framework

🔥SLAM, VIsual localization, keypoint detection, Image matching, Pose/Object tracking, Depth/Disparity/Flow Estimation, 3D-graphic, etc. related papers and code

Real-time 3D localization using a (velodyne) 3D LIDAR

Point cloud registration pipeline for robot localization and 3D perception

3D LIDAR Localization using NDT/GICP and pointcloud map in ROS 2 (Not SLAM)

WiLoR: End-to-end 3D hand localization and reconstruction in-the-wild

LOL: Lidar-only Odometry and Localization in 3D point cloud maps

Accurate 3D Localization for MAV Swarms by UWB and IMU Fusion. ICCA 2018

dddmr_navigation is the 3D navigation solution for mobile robots includes mapping/localization/perception/path planning/controller/move base

[ECCV 2020] ScanRefer: 3D Object Localization in RGB-D Scans using Natural Language

chen2020iros: Learning an Overlap-based Observation Model for 3D LiDAR Localization.

Learning Less is More - 6D Camera Localization via 3D Surface Regression

MonoGRNet: A Geometric Reasoning Network for Monocular 3D Object Detection and Localization | KITTI

Mobile Robot Localization in 3D Triangle Meshes & Geometric Scene Graphs

Monocular Camera Localization in Prior LiDAR Maps with 2D-3D Line Correspondences

Adaptive Monte Carlo Localization (AMCL) in 3D.

A ROS node of 3D-LiDAR-based Monte Carlo localization

Delving into Localization Errors for Monocular 3D Object Detection, CVPR'2021

MCPTAM is a set of ROS nodes for running Real-time 3D Visual Simultaneous Localization and Mapping (SLAM) using Multi-Camera Clusters. It includes tools for calibrating both the intrinsic and extrinsic parameters of the individual cameras within the rigid camera rig.

[CVPR2019]Occlusion-Net: 2D/3D Occluded Keypoint Localization Using Graph Networks

This project is the result of my undergraduate dissertation. The localization in dynamic environment is to deploy TensorRT optimized YOLOX in the front end of ORB-SLAM3 for object detection, and then eliminate all points belonging to the human bounding box. At the same time, the semantic information is sent to the mapping module to dye the 3D point cloud. The disadvantage of this project is that in the localization module, only the points belonging to people are processed, because people are dynamic most of the time. In the mapping module, we did not segment semantic objects accurately, resulting in wrong coloring of point clouds of other objects.

This repository is an implementation of the NeurIPS 2024 paper "LoD-Loc: Visual Localization using LoD 3D Map with Neural Wireframe Alignment".

[IROS 2025] GSplatLoc: Grounding Keypoint Descriptors into 3D Gaussian Splatting for Improved Visual Localization

[TVCG 2025] SplatLoc: 3D Gaussian Splatting-based Visual Localization for Augmented Reality

The 3D Toolkit provides algorithms and methods to process 3D point clouds. In includes automatic precise registration (6D simultaneous localization and mapping, 6D SLAM) and other tools, e.g., a fast 3D viewer, plane extraction software, etc.

Ray3D: ray-based 3D human pose estimation for monocular absolute 3D localization

Implementation of Monocular Direct Sparse Localization in a Prior 3D Surfel Map (DSL)

CVPR Paper - "3D Point Cloud Registration for Localization using a Deep Neural Network Auto-Encoder" - Partial Implementation Code

Awesome-3D/Multimodal-Anomaly-Detection-and-Localization/Segmentation/3D-KD/3D-knowledge-distillation