Mr. Chaz B. Cornwall Profile

Chaz B. Cornwall

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SPIE Journal Paper | 4 November 2022

Obstacle detection using range-difference events

Chaz Cornwall, Scott Budge

OE, Vol. 62, Issue 03, 031203, (November 2022) https://doi.org/10.1117/12.10.1117/1.OE.62.3.031203

KEYWORDS: LIDAR, Optical engineering, Magnetorheological finishing, Binary data, Sensors, Roads, Detection and tracking algorithms, Clouds, Roentgenium, Signal processing

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Obstacle detection (OD) for autonomous ground vehicles on highways and in dynamic environments is exceptionally difficult due to the short response time requirements. Many OD systems handle this problem by using sensors with less data or by using powerful processors. However, this problem can be solved using biology-inspired processing without replacing hardware. LiDAR is one of the most common sensor options for OD in autonomous ground vehicles. The importance map, a recently developed biology-inspired processing technique using events, has been able to efficiently highlight LiDAR returns in a scene that correspond to obstacles. Even though the importance map has shown potential, there are three significant flaws: no object movement distinction, high levels of noise, and poor static object tracking. Solutions for these flaws are presented in this research. The constant-angle principle identifies motion toward the ego vehicle, temporal filtering removes noise, and an updated static object-tracking algorithm performs well at various speeds. OD capabilities of the importance map and the previous importance map are compared using LiDAR data from the KITTI data set. Using comparisons between true-positive and false-positive rates, the importance map performs much better than the previous importance map. Furthermore, events are shown to be a highly discriminative feature for finding obstacles in a scene.

Proceedings Article | 6 June 2022 Presentation + Paper

Biology-inspired LiDAR-centric obstacle detection

Chaz Cornwall, Scott Budge

Proceedings Volume 12115, 1211502 (2022) https://doi.org/10.1117/12.2618675

KEYWORDS: LIDAR, Sensors, Signal processing, Detection and tracking algorithms, Algorithm development, Unmanned vehicles

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Efficiently and effectively processing LiDAR data at high speeds is a difficult problem that must be addressed if autonomous vehicles are to travel at speeds as high as their manned counterparts (i.e. over 80mph for ground vehicles on highways). Many processing algorithms avoid this "high throughput" problem by using a sensor with less spatial resolution or a more powerful (and more expensive) processor. This research is intended to help individuals who do not have these options when designing their obstacle detection pipeline.

The challenge of processing LiDAR data as quickly and efficiently as possible was addressed with the recently developed event map and importance map. These are images created from LiDAR scans that use biology-inspired principles to highlight areas in a scene that can be classified as obstacles. However, the importance map has three main flaws: there is no distinction among types of object movement, the output is extremely noisy, and static object tracking does not work well at high speeds.

This research reduces these three flaws by: implementing the constant-angle principle to identify motion towards the ego vehicle, using a recursive filter to remove noise, and deriving a new static object tracking algorithm to have consistent static object tracking. After implementing these changes, the new and old importance maps are compared using LiDAR data from the KITTI dataset. The importance maps are thresholded to create obstacle masks. Through comparison of true positive and false positive rates, the new importance map shows significant improvement over the previous implementation.

Proceedings Article | 12 April 2021 Presentation + Paper

A neuromorphic approach to LiDAR point cloud processing

Chaz Cornwall, Scott Budge

Proceedings Volume 11748, 1174805 (2021) https://doi.org/10.1117/12.2587540

KEYWORDS: LIDAR, Clouds, Data processing, Sensors, Imaging systems, Visible radiation, Spatial filters, Receptors, Optical filters, Cameras

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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.

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