Event Location: NSH 3305

Abstract: As the target scale of robot operations grows, so too does the challenge of developing software for such systems. It may be difficult, unsafe, or expensive to develop software on enough real-world conditions. Similarly, as the target applications of learning algorithms grow, so too do the challenges of gathering adequate training data. It may be difficult to collect large datasets, label them, or deal with different domains. Simulation has attracted attention as a solution to these problems. To be useful, simulators must have sufficient fidelity and flexibility. For the problem of off-road Lidar scene simulation, existing solutions are either high-fidelity, or flexible. Our work builds a Lidar simulator that is both.

Off-road Lidar simulation is challenging because of Lidar interaction with natural terrain such as vegetation. A hybrid geometric terrain representation, consisting of permeable ellipsoids and surface meshes, has been shown to model Lidar observations well. We propose to add semantic information to the hybrid geometric model, using standard procedures for point cloud segmentation and classification. This allows us to extract terrain primitives, such as trees and shrubs, from data logs. The primitives can then be used to compose unseen scenes to simulate sensor observations in. The advantage over arbitary mesh models of terrain is that the primitives are associated with sensor-realistic models obtained from real data.

A major use of simulators is to develop algorithms. In addition to measuring simulator fidelity at the level of observations, we present an algorithm-dependent risk. We formalize the notion that a good simulator must provide a developer useful feedback even when the algorithm has poor performance, just as real data would. We propose to apply the idea to develop a Lidar scan matching algorithm. In addition, we propose to use the simulator to train a CNN for off-road object recognition. Our handle on all aspects of fidelity will allow us to compare the utility of different simulators for developing algorithms.

Our approach is guided by past work on indoor Lidar simulation, and nonparameteric sensor modeling. Our datasets for training and test come from off-road sites of real-world interest. We expect our work to impact software development for off-road mobile robots, and add to the understanding of simulation in general.

Committee:Alonzo Kelly, Chair

Martial Hebert

Michael Kaess

Peter Corke, Queensland University of Technology