Airborne detection of vertical obstacles from sparse and cluttered point clouds

Paul R. Flanigen; Joseph M. Drockton; Ella M. Atkins

doi:10.1117/12.2632088

3 June 2022 Airborne detection of vertical obstacles from sparse and cluttered point clouds

Paul R. Flanigen, Joseph M. Drockton, Ella M. Atkins

Proceedings Volume 12114, Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping VII; 121140G (2022) https://doi.org/10.1117/12.2632088
Event: SPIE Defense + Commercial Sensing, 2022, Orlando, Florida, United States

Abstract

Vertical obstacles such as poles, towers, and wires present a significant hazard to low altitude flight in urban and tactical environments. The subtle protrusion of slender obstacles above surrounding terrain and buildings makes detection difficult. Current mitigation methods are not sufficient and necessitate an active sensor to enable obstacle avoidance. This paper surveys and assesses previous methods of vertical obstacle detection, including remote sensing, photogrammetry, and LIDAR sensors. Next, the paper examines the suitability of current point cloud segmentation methods, including clustering methods. Two new algorithms overcome unique aerial detection challenges. They broaden the safe volume by considering sparse data available from small obstacles at distances that will allow high flight speeds. Presented and existing clustering methodologies are evaluated against a variety of vertical obstacles in an Unreal Engine environment using Microsoft AirSim LIDAR simulation.

Conference Presentation

Citation Download Citation

Paul R. Flanigen, Joseph M. Drockton, and Ella M. Atkins "Airborne detection of vertical obstacles from sparse and cluttered point clouds", Proc. SPIE 12114, Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping VII, 121140G (3 June 2022); https://doi.org/10.1117/12.2632088

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