Learning Correspondences For Relative Pose Estimation
We present an end-to-end learnable, differentiable method for pairwise relative pose registration of RGB-D frames. Our method is robust to big camera motions thanks to a self-supervised weighting of the predicted correspondences between the frames. Given a pair of frames, our method estimates matches of points and their visibility score. A self-supervised model predicts a confidence weight for visible matches. Finally, visible matches and their weight are fed into a differentiable weighted Procrustes aligner which estimates the rigid transformation between the input frames.
Kinect Fusion: Dense Surface Mapping and Tracking
Implementation of the paper “Kinect Fusion: Real-Time Dense Surface Mapping and Tracking”
SLAM for autonomous vehicles
In this project, I worked on the SLAM pipeline for an autonomous driving vehicle. The tools I used for this project are, ROS, C++, PCL library, Ceres Solver, and Google Cartographer.