We present a proof-of-concept LIDAR design that allows adaptive real-time measurements according to dynamically specified measurement patterns. We describe our optical setup and calibration, which enables fast sparse depth measurements using a scanning MEMS (micro-electro-mechanical) mirror. We validate the efficacy of our prototype LIDAR design by testing on over 75 static and dynamic scenes spanning a range of environments. We show CNN-based depth-map completion experiments which demonstrate that our sensor can realize adaptive depth sensing for dynamic scenes.
@article{arxiv.2003.09545,
title = {Towards a MEMS-based Adaptive LIDAR},
author = {Francesco Pittaluga and Zaid Tasneem and Justin Folden and Brevin Tilmon and Ayan Chakrabarti and Sanjeev J. Koppal},
journal= {arXiv preprint arXiv:2003.09545},
year = {2020}
}
Comments
14 pages, 5 figures, project site: https://www.fpittaluga.com/adaptivelidar, to be published in International Conference on 3D Vision 2020