Academic Projects

We are preparing to use the direct method as in VO and formulate photometric error, I focused on photometric calibration when I learned and ported Direct Sparse Odometry Lite to our drone, which aims to provide localization in the wild and evaluate the importance of photometric calibration. I followed the manual photometric calibration paper from the TUM group and developed code to calculate the camera-response function and vignetting map. The calibration is proved to be useful for relative error metrics and prevents spikes in localization error.

I proved in details the convex relaxation portion of the paper Blind Deconvolution using Convex Program- ming and recreated its results. This project is completed with ECE273-convex optimization and greatly enhanced my understanding. My Instructor is Prof. Piya Pal.

I learned to use Isaac Gym and designed a network architecture that allows a high level network to select and combine appropriate low-level controllers. My network is proven to be beneficial. This is a course project instructed by Prof. Michael Yip.

A mini relational database implemented in C++ that supports sql-like commands with indexing and caching.

We designed PCB and control algorithm for this quadcopter which is ~14 cm in diagonal. This is a course project instructed by Prof. Steven Swanson.

Given stereo camera observation and IMU observations of a real self-driving dataset, I developed a sparse, feature based SLAM algorithm based on EKF and jointly estimation of agent pose and landmark positions. My Instructor is Prof. Nikolay Atanasov.

Given LIDAR and IMU observations of a real self-driving dataset, I developed a preliminary(no loop closure) SLAM algorithm based on occupancy map and particle filter. My Instructor is Prof. Nikolay Atanasov.