Dash Robotics Perception
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Updated
May 8, 2019 - C++
Dash Robotics Perception
Minimum code needed to run Autoware multi-object tracking
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.
Sensor Fusion Nanodegree | Lidar Obstacle Detection in Autonomous Vehicles
Point cloud segmentation with Azure Kinect
Improved pytorch implementation of RandLA (https://arxiv.org/abs/1911.11236) with easier transferability and reproductibility
Multiple command line utilities for working with tree point clouds, e.g. for computing the boxcounting dimension from point cloud data
[ROS package] Lightweight and Accurate Point Cloud Clustering
Final project titled "Point Cloud Segmentation and Object Tracking using RGB-D Data" for the Machine Vision (EE 576) course.
Robin C++ framework for semi-autonomous prosthesis control using computer vision
Fast and memory efficient semantic segmentation of 3D point clouds. Runs on Windows, Mac and Linux.
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