A comparison of data association techniques for Simultaneous Localization and Mapping

Abstract

The problem of Simultaneous Localization and Mapping (SLAM) has received a great deal of attention within the robotics literature, and the importance of the solutions to this problem has been well documented for successful operation of autonomous agents in a number of environments. Of the numerous solutions that have been developed for solving the SLAM problem many of the most successful approaches continue to either rely on, or stem from, the Extended Kalman Filter method (EKF). However, the new algorithm FastSLAM has attracted attention for many properties not found in EKF based methods. One such property is the ability to deal with unknown data association and its robustness to data association errors. The problem of data association has also received a great deal of attention in the robotics literature in recent years, and various solutions have been proposed. In an effort to both compare the performance of the EKF and FastSLAM under ambiguous data association situations, as well as compare the performance of three different data association methods a comprehensive study of various SLAM filter-data association combinations is performed. This study will consist of pairing the EKF and FastSLAM filtering approaches with the Joint Compatibility, Sequential Compatibility Nearest Neighbor, and Joint Maximum Likelihood data association methods. The comparison will be based on both contrived simulations as well as application to the publicly available Car Park data set. The simulated results will demonstrate a heavy dependence on geometry, particularly landmark separation, for the performance of both filter performance and the data association algorithms used.

(cont.) The real world data set results will demonstrate that the performance of some data association algorithms, when paired with an EKF, can give identical results. At the same time a distinction in mapping performance between those pairings and the EKF paired with Joint Compatibility data association will be shown. These EKF based pairings will be contrasted to the performance obtained for the FastSLAM- Sequential Nearest Neighbor marriage. Finally, the difficulties in applying the Joint Compatibility and Joint Maximum Likelihood data association methods using FastSLAM 1.0 for this data set will be discussed.