Robust Mapping and Localization in Indoor Environments Using Sonar Data

Abstract

In this paper we describe a new technique for the creation of featurebased stochastic maps using standard Polaroid sonar sensors. The fundamental contributions of our proposal are: (1) a perceptual grouping process that permits the robust identification and localization of environmental features, such as straight segments and corners, from the sparse and noisy sonar data; (2) a map joining technique that allows the system to build a sequence of independent limited-size stochastic maps and join them in a globally consistent way; (3) a robust mechanism to determine which features in a stochastic map correspond to the same environment feature, allowing the system to update the stochastic map accordingly, and perform tasks such as revisiting and loop closing. We demonstrate the practicality of this approach by building a geometric map of a medium size, real indoor environment, with several people moving around the robot. Maps built from laser data for the same experiment are provided for comparison.

Robocentric map joining: Improving the consistency of EKF-SLAM

“…Here we show that, not only is map joining computationally more efficient than building one global map from the beginning, as is it shown in [10], but it also allows one to attain better consistency in the stochastic map.…”

“…In [10] Tardós et al proposed a map building technique in which, instead of building one global map from the beginning of the exploration task, a sequence of local maps of limited size is built, and later joined together, to obtain the global map. Here we show that, not only is map joining computationally more efficient than building one global map from the beginning, as is it shown in [10], but it also allows one to attain better consistency in the stochastic map.…”

“…Because the robocentric local maps M R l F and M R l−1 E are built using independent information, they are uncorrelated [10]. Thus, we form a stacked state vector:…”

“…Many recent efforts have concentrated on reducing the computational complexity of SLAM in large environments [16,13,10,17]. However, only recently, the consistency issues of the EKF-SLAM algorithm have attracted the attention of the research community.…”

“…• It addresses the SLAM problem by building independent local maps of limited size, using the technique first proposed in [10]. This technique was shown to greatly reduce the computational cost of SLAM.…”