Quantitative and qualitative comparison of three laser-range mapping algorithms using two types of laser scanner data

Abstract

Presents our initial results in comparing three algorithms for autonomous robotic mapping using two types of laser scanner data. The algorithms compared are the Markov localization approach of S. Thrun et al. (1998), F. Lu and E. Milios's (1997) iterative dual correspondence algorithm, and C. Touzet's (2000) model-free landmark extraction algorithm. The two types of laser scanner data utilized are the AccuRange laser scanner from Acuity and the SICK laser scanner. We compare these algorithms in terms of the quality of mapped results, computational requirements, and sensitivity to data and odometry errors. While the complete comparison of these algorithms on all these measures is not yet accomplished, our results to date indicate that laser mapping algorithms are not immediately transferrable from one type of laser scanner data to another. Instead, algorithms appear to make implicit assumptions on the quality or content of laser data that play a strong role in the quality of the mapping results.