I engineered an advanced algorithm to generate noise filters for radar-based point cloud data incorporating XGBoost for radar data classification. I designed and implemented evaluation metrics for assessing point cloud clustering and object tracking performance from millimeter wave radar data using Python and NumPy. I contributed to the creation of a comprehensive computer vision-based object tracking dataset to establish ground truth for validating millimeter wave tracking accuracy.

I designed and deployed a high-precision point cloud processing pipeline for the production of sub-centimeter resolution, large-scale indoor digital elevation models. I developed a novel ceiling height detection algorithm leveraging deep learning-based semantic segmentation to handle complex multi-level environments with obstacles. I also extended the cloud API infrastructure to enable efficient storage and retrieval of large-scale raster datasets.

I integrated new GPS, IMU and LiDAR sensors with GRI's Localization and Mapping Platform (LAMP) while expanding the image reconstruction framework through GPS and AHRS based localization using an extended Kalman filter and the Robot Operating System (ROS) framework. Additionally, I led the optimization of core Docker infrastructure by implementing multi-stage builds and dependency management, which successfully reduced image size and build times.