Sensor Fusion Nanodegree | Lidar Obstacle Detection in Autonomous Vehicles
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Updated
Nov 29, 2020 - C++
Sensor Fusion Nanodegree | Lidar Obstacle Detection in Autonomous Vehicles
Robin C++ framework for semi-autonomous prosthesis control using computer vision
Multiple command line utilities for working with tree point clouds, e.g. for computing the boxcounting dimension from point cloud data
Dash Robotics Perception
Final project titled "Point Cloud Segmentation and Object Tracking using RGB-D Data" for the Machine Vision (EE 576) course.
Point cloud segmentation with Azure Kinect
In this project we detect, segment and track the obstacles of an ego car and its custom implementation of KDTree, obstacle detection, segmentation, clustering and tracking algorithm in C++ and compare it to the inbuilt algorithm functions of PCL library on a LiDAR's point cloud data.
Improved pytorch implementation of RandLA (https://arxiv.org/abs/1911.11236) with easier transferability and reproductibility
Fast and memory efficient semantic segmentation of 3D point clouds. Runs on Windows, Mac and Linux.
Minimum code needed to run Autoware multi-object tracking
[ROS package] Lightweight and Accurate Point Cloud Clustering
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