Andrew Willis
Papers
9
Total Citations
55
H-Index
4
About
Andrew Willis is a leading researcher in sensor fusion, 3D computer vision, and robotics, with a focus on integrating diverse sensing modalities for real-world applications. His most influential work, "A linear method for calibrating LIDAR-and-camera systems" (21 citations), provides a foundational mathematical model for explicitly integrating laser range scanner and camera data, enabling precise spatial alignment critical for autonomous systems. Willis has also advanced point cloud processing with his algorithm for real-time segmentation into planar regions (10 citations), a key capability for robotic navigation and mapping. His recent contributions include extrinsic calibration of camera and motion capture systems (6 citations) and the development of Cesium Tiles for high-realism simulation environments (4 citations), which allow researchers to test SLAM algorithms in virtual worlds before real-world deployment. Willis also created iGRaND, a novel RGBD feature descriptor for real-time tracking and motion capture, and the ROS georegistration package for aerial multispectral image simulation. With a portfolio spanning calibration, segmentation, simulation, and feature extraction, Willis’s work directly supports the robotics and computer vision communities by providing practical tools and theoretical insights that bridge the gap between simulation and reality.
Research Focus
Key Achievements
Top Papers
- 1A linear method for calibrating LIDAR-and-camera systems21 citations · 2009
- 2
- 3Extrinsic Calibration of Camera and Motion Capture Systems6 citations · 2021
- 4
- 5Measuring Compute-Reuse Opportunities for Video Processing Acceleration4 citations · 2019
- 6
- 7
- 8Quadrotor Flight Simulation in a CFD-generated Urban Wind Field2 citations · 2024
- 9Linear depth reconstruction for RGBD sensors2 citations · 2017