This project aims to reduce the fuzziness in point clouds generated by low-cost lidars by using priors and potentially adding imagers to the ranging measurements. The goal is to improve the fidelity of reconstructions while maintaining the compact and affordable form factor of the lidar devices.


Commercial lidars like the Velodyne Puck create a large number of range measurements — as many as 1 million/second — from small, compact, and relatively inexpensive form factors. The resulting point clouds are fuzzy, however, because even small patches in the environment are imaged from varying angles. To obtain sharper point clouds one may resort to more precise devices, but they tend to be larger, heavier, and more expensive, making it harder to incorporate them in robotic systems. In this project, students will demonstrate reduction of the spread of the point cloud by using priors and potentially by adding imagers to the ranging measurements. The resulting system will be a significant improvement over the state of the art in that low cost lidar can be used to produce high fidelity reconstructions of the environment.


The motivation of this project is to determine how to produce a high-quality point cloud by combining many low-quality point clouds:

The point clouds obtained from low-cost LiDARs often suffer from fuzziness, making them challenging to utilize effectively. Our primary objective is to reconstruct high-resolution point clouds from such fuzzy input, enabling more precise and reliable data analysis and applications.