Scan registration is an essential subtask when building maps based on range finder data from mobile robots. The problem is to deduce how the robot has moved between consecutive scans, based on the shape of overlapping portions of the scans. This paper presents a new algorithm for registration of 3D data. The algorithm is a generalization and improvement of the normal distributions transform ͑NDT͒ for 2D data developed by Biber and Strasser, which allows for accurate registration using a memory-efficient representation of the scan surface. A detailed quantitative and qualitative comparison of the new algorithm with the 3D version of the popular ICP ͑iterative closest point͒ algorithm is presented. Results with actual mine data, some of which were collected with a new prototype 3D laser scanner, show that the presented algorithm is faster and slightly more reliable than the standard ICP algorithm for 3D registration, while using a more memoryefficient scan surface representation.

This paper addresses the problem of simultaneous localization and mapping (SLAM) by a mobile robot. An incremental SLAM algorithm is introduced that is derived from multigrid methods used for solving partial differential equations. The approach improves on the performance of previous relaxation methods for robot mapping, because it optimizes the map at multiple levels of resolution. The resulting algorithm has an update time that is linear in the number of estimated features for typical indoor environments, even when closing very large loops, and offers advantages in handling nonlinearities compared with other SLAM algorithms. Experimental comparisons with alternative algorithms using two well-known data sets and mapping results on a real robot are also presented

Thanks to the efforts of the robotics and autonomous systems community, robots are becoming ever more capable. There is also an increasing demand from end-users for autonomous service robots that can operate in real environments for extended periods. In the STRANDS project we are tackling this demand head-on by integrating state-of-the-art artificial intelligence and robotics research into mobile service robots, and deploying these systems for long-term installations in security and care environments. Over four deployments, our robots have been operational for a combined duration of 104 days autonomously performing end-user defined tasks, covering 116km in the process. In this article we describe the approach we have used to enable long-term autonomous operation in everyday environments, and how our robots are able to use their long run times to improve their own performance

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Abstract-We present a method for introducing representation of dynamics into environment models that were originally tailored to represent static scenes. Rather than using a fixed probability value, the method models the uncertainty of the elementary environment states by probabilistic functions of time. These are composed of combinations of harmonic functions, which are obtained by means of frequency analysis. The use of frequency analysis allows to integrate long-term observations into memoryefficient spatio-temporal models that reflect the mid-to longterm environment dynamics. These frequency-enhanced spatiotemporal models allow to predict the future environment states, which improves the efficiency of mobile robot operation in changing environments. In a series of experiments performed over periods of days to years, we demonstrate that the proposed approach improves localization, path planning and exploration.

This paper addresses the problem of mapping the structure of a gas distribution by creating concentration gridmaps from the data collected by a mobile robot equipped with gas sensors. By contrast to metric gridmaps extracted from sonar or laser range scans, a single measurement from a gas sensor provides information about a comparatively small area. To overcome this problem, a mapping technique is introduced that uses a Gaussian weighting function to model the decreasing likelihood that a particular reading represents the true concentration with respect to the distance from the point of measurement. This method is evaluated in terms of its suitability regarding the slow response and recovery of the gas sensors, and experimental comparisons of different exploration strategies are presented. The stability of the mapped structures and the capability to use concentration gridmaps to locate a gas source are also discussed.

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Abstract-This paper introduces a dynamic map for mobile robots that adapts continuously over time. It resolves the stabilityplasticity dilemma (the trade-off between adaptation to new patterns and preservation of old patterns) by representing the environment over multiple timescales simultaneously (5 in our experiments). A sample-based representation is proposed, where older memories fade at different rates depending on the timescale. Robust statistics are used to interpret the samples. It is shown that this approach can track both stationary and non-stationary elements of the environment, covering the full spectrum of variations from moving objects to structural changes. The method was evaluated in a five week experiment in a real dynamic environment. Experimental results show that the resulting map is stable, improves its quality over time and adapts to changes.