A robot exploring an unknown environmentmay need to build a world model from sensor measurements. In order to integrate all the frames of sensor data, it is essential to align the data properly. An incremental approach has been typically used in the past, in which each local frame of data is aligned to a cumulative global model, and then merged to the model. Because different parts of the model are updated independently while there are errors in the registration, such an approachmay result in an inconsistent model. In this paper, we study the problem of consistent registration of multiple frames of measurements (range scans), together with the related issues of representation and manipulation of spatial uncertainties. Our approachistomaintain all the local frames of data as well as the relative spatial relationships between local frames. These spatial relationships are modeled as random variables and are derived from matching pairwise scans or from odometry. Then we formulat...

A mobile robot exploring an unknown environment has no absolute frame of reference for its position, other than features it detects through its sensors. Using distinguishable landmarks is one possible approach, but it requires solving the object recognition problem. In particular, when the robot uses two-dimensional laser range scans for localization, it is difficult to accurately detect and localize landmarks in the environment (such as corners and occlusions) from the range scans. In this paper, we develop two new iterative algorithms to register a range scan to a previous scan so as to compute relative robot positions in an unknown environment, that avoid the above problems. The first algorithm is based on matching data points with tangent directions in two scans and minimizing a distance function in order to solve the displacementbetween the scans. The second algorithm establishes correspondences between points in the two scans and then solves the point-to-point least-squares probl...

This paper describes results from evaluating different self-localization approaches in indoor environments for mobile robots. The examined algorithms are based on 2d laser scans and an odometry position estimate and do not need any modifications in the environment. Due to the goals of our project an important requirement for self-localization is the ability to cope with office-like environments as well as with environments without orthogonal and rectilinear walls. Furthermore, the approaches have to be robust enough to cope with slight modifications in the daily environment and should be fast enough to be used on-line on board of the robot system. To fulfil these requirements we made some extensions to existing approaches and combined them in a suitable manner. Real world experiments with our robot within the everyday environment of our institute show that the position error can be kept small enough to perform navigation tasks. Keywords: Mobile Robot, Self-Localization 1.

This paper describes a novel, laser-based approach for tracking the pose of a high-speed mobile robot. The algorithm is outstanding in terms of accuracy and computation time. The efficiency is achieved by a closed-form solution for the matching of two laser scans, the use of natural scan features and fast linear filters. The implemented algorithm is evaluated with the high-speed robot Kurt3D (4 m/s), and compared to standard scan matching methods in indoor and outdoor environments.

www.elsevier.com/locate/robot This paper presents Scan-SLAM, a new generalization of simultaneous localization and mapping (SLAM). SLAM implementations based on extended Kalman filter (EKF) data fusion have traditionally relied on simple geometric models for defining landmarks. This limits EKF-SLAM to environments suited to such models and tends to discard much potentially useful data. The approach presented in this paper is a marriage of EKF-SLAM and scan correlation. Landmarks are no longer defined by analytical models; instead they are defined by templates composed of raw sensed data. These templates can be augmented as more data become available so that the landmark definition improves with time. A new generic observation model is derived that is generated by scan correlation, and this permits stochastic location estimation for landmarks with arbitrary shape within the Kalman filter framework. The statistical advantages of an EKF representation are augmented with the general applicability of scan matching. Scan matching also serves to enhance data association reliability by providing a shape metric for landmark disambiguation. Experimental results in an outdoor environment are presented which validate the algorithm. c ○ 2006 Elsevier B.V. All rights reserved. Keywords: Simultaneous localisation and mapping (SLAM); EKF-SLAM; Scan correlation

Abstract — This paper presents a novel approach to mapping for mobile robots that exploits user interaction to semiautonomously create a labelled map of the environment. The robot autonomously follows the user and is provided with a verbal commentary on the current location with phrases such as “Robot, we are in the office”. At the same time, a metric feature map is generated using fusion of laser and advanced sonar measurements in a Kalman filter based SLAM framework, which is later used for localization. When mapping is complete, the robot generates an occupancy grid for use in global task planning. The occupancy grid is created using a novel laser scan registration scheme that relies on storing the path of the robot along with associated local SLAM features during mapping, and later recovering the path by matching the associated local features to the final SLAM map. The occupancy grid is segmented into labelled rooms using an algorithm based on watershed segmentation and integration of the verbal commentary. Experimental results demonstrate our mobile robot creating SLAM and segmented occupancy grid maps of rooms along a 70 metre corridor, and then using these maps to navigate between rooms. Index Terms — SLAM, advanced sonar, laser, occupancy map, room segmentation

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This paper presents an approach to save navigation of autonomous mobile systems within partially known or unknown dynamic environments. In this context, faulty, maladjusted and otherwise influenced sensors must be recognized (error detection), and adequate measures for failure correction (error recovery) must be taken. Furthermore, reliable recognition of objects which are diJficult to detect must be achieved.

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This paper describes a method for identifying an environment a robot is operating in by comparing the geometry of landmarks of a map the robot is currently building with a set of previously created maps. Landmark maps are created using the stochastic map approach originally presented by Smith, Self and Cheeseman [12]. The paper provides a method for measuring the similarity of such maps and presents a closed form solution for the special case that covariances are constant. Experiments carried out on a developer version of the AIBO robot and on a prototype of the humanoid SDR-4X robot show that the approach is applicable even for robots with poor odometry.