Date of Award

2023

Document Type

Open Access Dissertation

Degree Name

Doctor of Philosophy in Electrical Engineering (PhD)

Administrative Home Department

Department of Electrical and Computer Engineering

Advisor 1

Jeremy Bos

Committee Member 1

Darrell Robinette

Committee Member 2

Michael Roggemann

Committee Member 3

Anthony Pinar

Abstract

Safe and robust operation of autonomous ground vehicles in all types of conditions and environment necessitates complex perception systems and unique, innovative solutions. This work addresses automotive lidar and maximizing the performance of a simultaneous localization and mapping stack. An exploratory experiment and an open benchmarking experiment are both presented. Additionally, a popular SLAM application is extended to use the type of information gained from lidar characterization, demonstrating the performance gains and necessity to tightly couple perception software and sensor hardware. The first exploratory experiment collects data from child-sized, low-reflectance targets over a range from 15 m to 35 m. The targets are placed in close proximity with respect to the azimuthal sweep of scanning lidars in an effort to stress the sensor’s gain controllers. Results are presented for range variance with respect to target distance, the propagation of beam spot size, an analysis on the resolvability of target width, and a convolutional analysis of the interaction between the incident beam and the leading and trailing edges of the targets. The range distributions are not normal, and often appears as an ‘H’ when viewed from the top-down. The second experiment was an open benchmarking experiment. Data was collected using calibrated child-size targets. The targets have lambertian reflections and are low-reflectance, and calibrated to maintain those characteristics across the range of lidar wavelengths tested. The nearest target during this experiment was at 5 m while the furthest was at 200 m. A benchmarking test with the targets alone revealed range biases varying from −4.21 cm to 8.16 cm. Average range precision was between 1.33 cm and 7.57 cm. A second benchmark was taken with confusers placed adjacent to the targets. These confusers were retroreflective and varied in size. Presence of the confusers increased the range of average range bias between −4.96 cm and 8.42 cm. The precision was reduced, having a range of 2.22 cm to 7.57 cm. Furthermore, the number of detections or points on target, were reduced, sometimes by more than 20%. The third work presented is LIOSAM-LH. It is an extension, using lidar heuristics, of the state-of-the-art lidar-inertial SLAM framework, LIO-SAM. LIOSAM-LH uses GTSAM to provide a factor-graph based odometry solution. Two key contributions, a correction layer and a factor-augmentation layer, enable increased map accuracy, more stable odometry tracks, and reduced computation times. Specifically, the corrections layer reduces artificial inflation of objects in the pointcloud map by greater than 30%. The solution stability, or more aptly, how much the track moves when the graph is exposed to degraded GPS factors, was reduced by orders of magnitude. This translates to error on the scale of 1 m or more when GPS drops out. Lastly, the total time required for factor graph optimization was reduced by between 7 and 26%, per step.

Creative Commons License

Creative Commons Attribution-Share Alike 4.0 License
This work is licensed under a Creative Commons Attribution-Share Alike 4.0 License.

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