Iterated D-SLAM Map Joining -- Evaluating its performance in terms of consistency, accuracy and efficiency

Shoudong Huang, Zhan Wang, Gamini Dissanayake, Udo Frese

In: Autonomous Robots 27 4 Seiten 409-429 Springer Netherlands 2009.


This paper presents a new map joining algorithm and a set of metrics for evaluating the performance of mapping techniques. The input to the new map joining algorithm is a sequence of local maps containing the feature positions and the final robot pose in a local frame of reference. The output is a global map containing the global positions of all the features but without any robot poses. The algorithm builds on the D-SLAM mapping algorithm [1] and uses iterations to improve the estimates in the map joining step. So it is called Iterated D-SLAM Map Joining (I-DMJ). When joining maps I-DMJ ignores the odometry information connecting successive maps. This is the key to I-DMJ e±ciency, because it makes both the information matrix exactly sparse and the size of the state vector bounded by the number of features. The paper proposes metrics for quantifying the performance of different mapping algorithms focusing on evaluating their consistency, accuracy and efficiency. The I-DMJ algorithm and a number of existing SLAM algorithms are evaluated using the proposed metrics. The simulation data sets and a preprocessed Victoria Park data set used in this paper are made available to enable interested researchers to compare their mapping algorithms with I-DMJ.

huang_ar_09.pdf (pdf, 9 MB )

Deutsches Forschungszentrum für Künstliche Intelligenz
German Research Center for Artificial Intelligence