One of the goals in the field of mobile robotics is the development of mobile platforms which operate in populated environments. For many tasks it is therefore highly desirable that a robot can track the positions of the humans in its surrounding. In this paper we introduce sample-based joint probabilistic data association filters as a new algorithm to track multiple moving objects. Our method applies Bayesian filtering to adapt the tracking process to the number of objects in the perceptual range of the robot. The approach has been implemented and tested on a real robot using laser-range data. We present experiments illustrating that our algorithm is able to robustly keep track of multiple persons. The experiments furthermore show that the approach outperforms other techniques developed so far.

This paper presents a new sensing modality for multirobot exploration. The approach is based on using a pair of robots that observe each other, and act in concert to reduce odometry errors. We assume the robots can both directly sense nearby obstacles and see each other. The proposed approach improves the quality of the map by reducing the inaccuracies that occur over time from dead reckoning errors. Furthermore, by exploiting the ability of the robots to see each other, we can detect opaque obstacles in the environment independently of their surface reectance properties. Two dierent algorithms, based on the size of the environment, are introduced, with a complexity analysis, and experimental results in simulation and with real robots. Keywords: Exploration, Mapping, Multiple Robots, Cooperative Localization. 1. Introduction In this paper we discuss the benets of cooperative localization during the exploration of a large environment. A new

Over the last years, particle filters have been applied with great success to a variety of state estimation problems. In this paper we present a statistical approach to increasing the efficiency of particle filters by adapting the size of sample sets during the estimation process.

This paper presents an algorithm for full 3D shape reconstruction of indoor and outdoor environments with mobile robots. Data is acquired by a fastmoving robot equipped with two 2D laser range finders. Our approach combines an efficient scan matching routine for robot pose estimation with an algorithm for approximating environments using flat surfaces. On top of that, our approach includes a mesh simplification technique to reduce the complexity of the resulting models. In extensive experiments, our method is shown to produce accurate models of indoor and outdoor environments that compare favorably to other methods.

Mobile robots require the ability to build their own maps to operate in unknown environments. A funda-mental problem is that odometry-based dead reckoning cannot be used to assign accurate global position infor-mation to a map because of drift errors caused by wheel slippage. This paper introduces a fast, on-line method of learning globally consistent maps, using only local metric information. The approach differs from previ-ous work in that it is computationally cheap, easy to implement and is guaranteed to find a globally optimal solution. Experiments are presented in which large, complex environments were successfully mapped by a real robot, and quantitative performance measures are used to assess the quality of the maps obtained. 1

Localization is one of the fundamental problems of mobile robots. In order to efficiently perform useful tasks such as office delivery, mobile robots must know their position in their environment. Existing approaches can be distinguished according to the type of localization problem they are designed to solve. Tracking techniques aim at monitoring the robot’s position. They assume that the position is initially known and cannot recover from situations in which they lost track of the robot’s position. Global localization techniques, on the other hand, are able to estimate the robot’s position under complete uncertainty. In this paper we present the dynamic Markov localization technique as a uniform approach to position estimation, which is able (1) to globally estimate the position of the robot, (2) to efficiently track its position whenever the robot’s certainty is high, and (3) to detect and recover from localization failures. The approach has been implemented and intensively tested in real-world environments. We present several experiments illustrating the strength of our method. 1.

Matching 2D range scans is a basic component of many localization and mapping algorithms. Most scan match algorithms require finding correspondences between the used features, i.e. points or lines. We propose an alternative representation for a range scan, the Normal Distributions Transform. Similar to an occupancy grid, we subdivide the 2D plane into cells. To each cell, we assign a normal distribution, which locally models the probability of measuring a point. The result of the transform is a piecewise continuous and differentiable probability density, that can be used to match another scan using Newton's algorithm. Thereby, no explicit correspondences have to be established. We present the algorithm in detail and show the application to relative position tracking and simultaneous localization and map building (SLAM). First results on real data demonstrate, that the algorithm is capable to map unmodified indoor environments reliable and in real time, even without using odometry data.

Abstract. In this paper, we describe an automated method for fast, ground-based acquisition of large-scale 3D city models. Our experimental set up consists of a truck equipped with one camera and two fast, inexpensive 2D laser scanners, being driven on city streets under normal traffic conditions. One scanner is mounted vertically to capture building facades, and the other one is mounted horizontally. Successive horizontal scans are matched with each other in order to determine an estimate of the vehicle’s motion, and relative motion estimates are concatenated to form an initial path. Assuming that features such as buildings are visible from both ground-based and airborne view, this initial path is globally corrected by Monte-Carlo Localization techniques. Specifically, the final global pose is obtained by utilizing an aerial photograph or a Digital Surface Model as a global map, to which the ground-based horizontal laser scans are matched. A fairly accurate, textured 3D cof the downtown Berkeley area has been acquired in a matter of minutes, limited only by traffic conditions during the data acquisition phase. Subsequent automated processing time to accurately localize the acquisition vehicle is 235 minutes for a 37 minutes or 10.2 km drive, i.e. 23 minutes per kilometer. Keywords: laser scanning, navigation, self-localization, mobile robots, 3D modeling, Monte-Carlo localization 1.

Evidence gridsprovide a uniform representation for fusing temporally and spatially distinct sensor readings. However, the use of evidence grids requires that the robot be localized within its environment. Odometry errors typically accumulate over time, making localization estimates degrade, and introducing signi-cant errors into evidence grids as they are built. We have addressed this problem by developing a method for \continuous localization", in which the robot corrects its localization estimates incrementally and on the y. Assuming the mobile robot has a map of its environment represented as an evidence grid, localization is achieved by building a series of \local perception grids " based on localized sensor readings and the current odometry, and then registering the local and global grids. The registration produces an o set which is used to correct the odometry. Results are given on the e ectiveness of this method, and quantify the improvement of continuous localization over dead reckoning. We also compare di erent techniques for matching evidence grids and for searching registration o sets. 1

Localization is a critical issue in mobile robotics. If the robot does not know where it is, it, cannot effectively plan movements, locate objects, or reach goals. In this paper, we describe probabilistic self-localization techniques for mobile robots that are based on the principal of maximum-likelihood estimation. The basic method is to compare a map generated at the current robot position to a previously generated map of the environment to prohabilistically maximize the agreement between the maps. This method is able to operate in both indoor and outdoor environments using either discrete features or an occupancy grid to represent the world map. The map may be generated using any method to detect features in the robot's surroundings, including vision, sonar, a d laser range-finder. A global search of the pose space is performed that guarantees that the best position in a discretized pose space is found according to the probabilistic: map agreement measure. In addition, fitting the likelihood function with a parameterized smface allows both subpixel localization and uncertainty estimation to be performed. The application of these techniques in several experiments is described, including experimental localization results for the Sojourner Mars rover. 1