Probabilistic cooperative localization and mapping in practice

Abstract

In this paper we present a probabilistic framework for the reduction in the uncertainty of a moving robot pose during exploration by using a second robot to assist. A Monte Carlo Simulation technique (specifically, a Particle Filter) is employed in order to model and reduce the accumulated odometric error. Furthermore, we study the requirements to obtain an accurate yet timely pose estimate. A team of two robots is employed to explore an indoor environment in this paper, although several aspects of the approach have been extended to larger groups. The concept behind our exploration strategy has been presented previously and is based on having one robot carry a sensor that acts as a "robot tracker" to estimate the position of the other robot. By suitable use of the tracker as an appropriate motion-control mechanism we can sweep areas of free space between the stationary and the moving robot and generate an accurate graph-based description of the environment. This graph is used to guide the exploration process. Complete exploration without any overlaps is guaranteed as a result of the guidance provided by the dual graph of the spatial decomposition (triangulation) of the environment. We present experimental results from indoor experiments in our laboratory and from more complex simulated experiments.

Collective decision-making based on social odometry

“…Therefore, the environment must be properly configured before setting up the experiment. In Rekleitis et al (2003), the robots explore the environment in teams of two. Each team of two robots take turns moving so that at any time one is stationary and acts as a fixed reference point to the robot which moves.…”

Propagation of Uncertainty in Cooperative Multirobot Localization: Analysis and Experimental Results

“…This issue is discussed in detail in [27]. A particle filter was also used in the context of multi-robot exploration in [26].…”

An overview of AUV algorithms research and testbed at the University of Michigan

“…While these techniques can often be extended to a centralized multi-agent framework [10] (provided that there are no communication bandwidth restrictions), the extension of single-agent techniques to a decentralized multi-vehicle SLAM framework is often not obvious nor appropriate. Much of the previous research in the area of distributed multi-vehicle SLAM has focused primarily on terrestrial (i.e., land and aerial) applications [11]- [15], where high-bandwidth radio communication is possible; however, underwater communication bandwidth is distinctly limited from that on land [16].…”