Implementation of Stereo Visual Odometry using Classical Computer Vision techniques on KITTI Benchmark Dataset
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Updated
May 27, 2024 - C++
Implementation of Stereo Visual Odometry using Classical Computer Vision techniques on KITTI Benchmark Dataset
Line detection from images, object clustering and point cloud triangulation
A Stereo Visual Odometry on KITTI dataset
This is a Visual SLAM project that was undertaken as part of the EECE 5554 - Robotics Sensing and Navigation course at Northeastern University
Implementation of stereo visual odometry with bundle adjustment using classical computer vision algorithms and optimization techniques on KITTI dataset.
Stereo Visual Odometry using Kitti Dataset
Stereo visual odometry using Kitty Dataset
Implementation of rangenet++ (Dockerfile). Point cloud segmentation
Detect and track objects from the benchmark KITTI dataset. Classify those objects and project them into three dimensions. Fuse those projections together with LiDAR data to create 3D objects to track over time.
A LiDAR plugin for GTA 5 that generates data similar to KITTI data set.
Evaluation code for KITTI
C++ parser for the RAW KITTI dataset, with callbacks
Add support OpenLoris Datasets with mono + odom
Data visualizer for Kitti dataset.
An attempt to implement a simple monocular camera based visual odometry from scratch
A real-time LiDAR SLAM package that integrates FLOAM and ScanContext.
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