Efficient and parallel algorithms for point cloud registration [C++, Python]
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Updated
Jul 1, 2024 - C++
Efficient and parallel algorithms for point cloud registration [C++, Python]
Pointcloud-based graph SLAM written in C++ using open3D library.
Point cloud map evaluation library for the FusionPortable dataset. Metrics include Mean Map Entropy (MME), RMSE, Accuracy(mean error), Precision(standard deviation), completeness(overlap ratio), chamfer distance and F1-score at all levels of 1/2/5/10/20cm.
Error State Kalman Filter based Loosely-Coupled Lidar-IMU Odometry
Implementation of ICP, Point-to-Plane ICP, and G-ICP algorithms.
此项目基于 Open3D 和 Azure Kinect DK 实现了三维重建。基于 IMU 传感器实现粗配准,基于彩色 ICP 算法实现精配准。
Projects developed during gaining the self-driving car nano-degree from Udacity
See `releases` for binary wheels for ubuntu 16.04
A real-world hiking* map** using unreal engine. (*no hiking, **barely a map)
Implementation of ICCV 2017: Colored Point Cloud Registration Revisited
T-LOAM: Truncated Least Squares Lidar-only Odometry and Mapping in Real-Time
Visualization of local reference frames computed with FLARE
An example on how to implement mouse interaction on the Open3D visualizer, and how to use it to pick voxels.
3-Dimensional registration utilities for Sonia Rao's THINC Lab @ UGA research project
Bachelor's thesis project with the aim of creating a tool that can offer the possibility of evaluate the accuracy of classifications identified in high-density point clouds
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