3D LiDAR Object Detection & Tracking using Euclidean Clustering, RANSAC, & Hungarian Algorithm
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Updated
Mar 20, 2024 - C++
3D LiDAR Object Detection & Tracking using Euclidean Clustering, RANSAC, & Hungarian Algorithm
A unified library for 3D data processing with both c++ and python API
The project’s main goal is to investigate real-time object detection and tracking of pedestrians or bicyclists using a Velodyne LiDAR Sensor. Various point-cloud-based algorithms are implemented using the Open3d python package. The resulting 3D point cloud can then be processed to detect objects in the surrounding environment.
A unified library for fitting primitives from 3D point cloud data with both C++&Python API.
This c++ implementation RANSAC algorithm finds the n best fitting circles out of the given points.
This is an open source library that can be used to autofocus telescopes. It uses a novel algorithm based on robust statistics. For a preprint, see https://arxiv.org/abs/2201.12466 .The library is currently used in Astro Photography tool (APT) https://www.astrophotography.app/
CPU implementation of the Image stitching using FAST. For FPGA implementation visit tharaka27-SocStitcher.
Point Cloud processing (VoxelGrid Downsampling, RANSAC Segmentation, KDTree Euclidean Clustering) for obstacle detection for autonomous vehicles.
Computer Vision
Feature matching (SIFT) between two images and then applying normalized linear homography estimation, robustified by standard RANSAC
Image matching points finder based on RANSAC method, AI course at Wroclaw University of Science and Technology. Implementation is done in C++ with QT lib serving as visualization tool.
Fit RANSAC lines on edge detected images - Sequential RANSAC
curve line fitting using ransac or ceres
Line detection from images, object clustering and point cloud triangulation
Project based on Random sample consensus RANSAC
implementation of RANSAC for 2D points in C++ with testing/verification code in python
Choosing inliers with RANSAC algorithm
Representing objects in 3D dimensional space using feature extraction and Unity engine.
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