Performance analysis for stable mobile robot navigation solutions

Abstract

Robot navigation in complex, dynamic and unstructured environments demands robust mapping and localization solutions. One of the most popular methods in recent years has been the use of scan-matching schemes where temporally correlated sensor data sets are registered for obtaining a Simultaneous Localization and Mapping (SLAM) navigation solution. The primary bottleneck of such scan-matching schemes is correspondence determination, i.e. associating a feature (structure) in one dataset to its counterpart in the other. Outliers, occlusions, and sensor noise complicate the determination of reliable correspondences. This paper describes testing scenarios being developed at NIST to analyze the performance of scan-matching algorithms. This analysis is critical for the development of practical SLAM algorithms in various application domains where sensor payload, wheel slippage, and power constraints impose severe restrictions. We will present results using a high-fidelity simulation testbed, the Unified System for Automation and Robot Simulation (USARSim).