A neuromorphic approach to LiDAR point cloud processing

Chaz B. Cornwall; Scott E. Budge

doi:10.1117/12.2587540

12 April 2021 A neuromorphic approach to LiDAR point cloud processing

Chaz B. Cornwall, Scott E. Budge

Proceedings Volume 11748, Autonomous Systems: Sensors, Processing, and Security for Vehicles and Infrastructure 2021; 1174805 (2021) https://doi.org/10.1117/12.2587540
Event: SPIE Defense + Commercial Sensing, 2021, Online Only

Abstract

Most LiDAR point cloud processing techniques continue to gather more data as the data is available. This is also typical in most imaging systems, especially visible light camera systems. We propose a computationally efficient solution where data only continues to be processed if the data has changed. Once points are received by the LiDAR hardware driver, a sensor frame spatial event filter is used to compare a previous point with the most recent point obtained from that same coordinate in the LiDAR's receptor array. The output of the event filter then fills an array of events, or event map, that will be accessible by a layer of neurons that can be implemented in a GPU. The operations per point are compared between this event-based solution and other similar solutions. We show the event-based solution's efficiency can be better, according to how much the scene is changing and how many post-processing steps are involved. Point cloud data is collected from a LiDAR mounted on a vehicle driving in paved road conditions to illustrate the concept.

Conference Presentation

Citation Download Citation

Chaz B. Cornwall and Scott E. Budge "A neuromorphic approach to LiDAR point cloud processing", Proc. SPIE 11748, Autonomous Systems: Sensors, Processing, and Security for Vehicles and Infrastructure 2021, 1174805 (12 April 2021); https://doi.org/10.1117/12.2587540

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