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22
Robust Monte Carlo Localization for Mobile Robots
, 2001
"... Mobile robot localization is the problem of determining a robot's pose from sensor data. This article presents a family of probabilistic localization algorithms known as Monte Carlo Localization (MCL). MCL algorithms represent a robot's belief by a set of weighted hypotheses (samples), whi ..."
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Cited by 826 (88 self)
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Mobile robot localization is the problem of determining a robot's pose from sensor data. This article presents a family of probabilistic localization algorithms known as Monte Carlo Localization (MCL). MCL algorithms represent a robot's belief by a set of weighted hypotheses (samples), which approximate the posterior under a common Bayesian formulation of the localization problem. Building on the basic MCL algorithm, this article develops a more robust algorithm called MixtureMCL, which integrates two complimentary ways of generating samples in the estimation. To apply this algorithm to mobile robots equipped with range finders, a kernel density tree is learned that permits fast sampling. Systematic empirical results illustrate the robustness and computational efficiency of the approach.
Estimating the Absolute Position of a Mobile Robot Using Position Probability Grids
 In Proceedings of the Thirteenth National Conference on Artificial Intelligence, Menlo Park
, 1996
"... In order to reuse existing models of the environment mobile robots must be able to estimate their position and orientation in such models. Most of the existing methods for position estimation are based on special purpose sensors or aim at tracking the robot's position relative to the known sta ..."
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Cited by 200 (47 self)
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In order to reuse existing models of the environment mobile robots must be able to estimate their position and orientation in such models. Most of the existing methods for position estimation are based on special purpose sensors or aim at tracking the robot's position relative to the known starting point. This paper describes the position probability grid approach to estimating the robot's absolute position and orientation in a metric model of the environment. Our method is designed to work with standard sensors and is independent of any knowledge about the starting point. It is a Bayesian approach based on certainty grids. In each cell of such a grid we store the probability that this cell refers to the current position of the robot. These probabilities are obtained by integrating the likelihoods of sensor readings over time. Results described in this paper show that our technique is able to reliably estimate the position of a robot in complex environments. Our approach has proven to...
Fast Gridbased Position Tracking for Mobile Robots
 In Proc. of the 21th German Conference on Artificial Intelligence
, 1997
"... Abstract. One of the fundamental problems in the eld of mobile robotics is the estimation of the robot's position in the environment. Position probability grids have been proven to be a robust technique for the estimation of the absolute position of a mobile robot. In this paper we describe an ..."
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Cited by 23 (8 self)
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Abstract. One of the fundamental problems in the eld of mobile robotics is the estimation of the robot's position in the environment. Position probability grids have been proven to be a robust technique for the estimation of the absolute position of a mobile robot. In this paper we describe an application of position probability grids to position tracking. Given a starting position our approach keeps track ofthe robot's current position by matching sensor readings against a metric model of the environment. The method is designed to work with noisy sensors and approximative models of the environment. Furthermore, it is able to integrate sensor readings of di erent types of sensors over time. By using raw sensor data, the method exploits arbitrary features of the environment and, in contrast to many other approaches, is not restricted to a xed set of prede ned features such as doors, openings or corridor junction types. An adaptable sensor model allows a fast integration of new sensings. The results described in this paper illustrate the robustness of our method in the presence of sensor noise and errors in the environmental model. 1
Scan matching in the Hough domain
 in Proceedings of the IEEE International conference on Robotics and Automation (ICRA
, 2005
"... Abstract — Scan matching is used as a building block in many robotic applications, for localization and simultaneous localization and mapping (SLAM). Although many techniques have been proposed for scan matching in the past years, more efficient and effective scan matching procedures allow for impro ..."
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Cited by 20 (4 self)
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Abstract — Scan matching is used as a building block in many robotic applications, for localization and simultaneous localization and mapping (SLAM). Although many techniques have been proposed for scan matching in the past years, more efficient and effective scan matching procedures allow for improvements of such associated problems. In this paper we present a new scan matching method that, exploiting the properties of the Hough domain, allows for combining advantages of dense scan matching algorithms with featurebased ones. Index Terms — Scan matching, Hough Transform I.
HighSpeed Laser Localization For Mobile Robots
, 2005
"... This paper describes a novel, laserbased approach for tracking the pose of a highspeed mobile robot. The algorithm is outstanding in terms of accuracy and computation time. The efficiency is achieved by a closedform solution for the matching of two laser scans, the use of natural scan features an ..."
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Cited by 20 (1 self)
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This paper describes a novel, laserbased approach for tracking the pose of a highspeed mobile robot. The algorithm is outstanding in terms of accuracy and computation time. The efficiency is achieved by a closedform 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 highspeed robot Kurt3D (4 m/s), and compared to standard scan matching methods in indoor and outdoor environments.
On comparing the power of robots
 International Journal of Robotics Research. Under review
"... Robots must complete their tasks in spite of unreliable actuators and limited, noisy sensing. In this paper, we consider the information requirements of such tasks. What sensing and actuation abilities are needed to complete a given task? Are some robot systems provably “more powerful, ” in terms of ..."
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Cited by 15 (6 self)
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Robots must complete their tasks in spite of unreliable actuators and limited, noisy sensing. In this paper, we consider the information requirements of such tasks. What sensing and actuation abilities are needed to complete a given task? Are some robot systems provably “more powerful, ” in terms of the tasks they can complete, than others? Can we find meaningful equivalence classes of robot systems? This line of research is inspired by the theory of computation, which has produced similar results for abstract computing machines. The basic idea is a dominance relation over robot systems that formalizes the idea that some robots are stronger than others. This comparison, which is based on the how the robots progress through their information spaces, induces a partial order over the set of robot systems. We prove some basic properties of this partial order and show that it is directly related to the robots’ ability to complete tasks. We give examples to demonstrate the theory, including a detailed analysis of a limitedsensing global localization problem. 1
Keypoint Design and Evaluation for Place Recognition in 2D Lidar Maps
"... Abstract—Place recognition addresses the problem of determining whether a robot is in a map, and if so, globally localizing, without being given any prior estimate. An efficient method of solving this problem involves selecting a set of keypoints which encode the local region, and then utilizing a s ..."
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Cited by 12 (1 self)
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Abstract—Place recognition addresses the problem of determining whether a robot is in a map, and if so, globally localizing, without being given any prior estimate. An efficient method of solving this problem involves selecting a set of keypoints which encode the local region, and then utilizing a sublineartime nearest neighbors search into a database of keypoints previously generated from the global map to find places with common features. We present an algorithm to embed arbitrary keypoint descriptors in a metric space, which is required in order to frame the problem as a nearest neighbor search. Given that there are a multitude of possibilities for keypoint design, we propose a general methodology for comparing keypoint location selection heuristics and descriptor models that describe the region around the keypoint. With respect to keypoint locations, we introduce a metric that encodes how likely it is that the keypoint will be found in independent mapping passes given the presence of noise and occlusions. Metrics for keypoint descriptors are used to assess the separation between the distributions of matches and nonmatches and the probability the correct match will be found in a knearest neighbors search. We apply our design evaluation methodology to three keypoint selection heuristics and five keypoint descriptor models. Verification of the test outcomes is done by comparing the various keypoint designs on a kilometersscale place recognition problem. I.
Temporal Range Registration for Unmanned Ground and Aerial Vehicles
 In Proceedings of the IEEE International Conference on Robotics and Automation
, 2004
"... Abstract — An iterative temporal registration algorithm is presented in this paper 1 for registering 3D range images obtained from unmanned ground and aerial vehicles traversing unstructured environments. We are primarily motivated by the development of 3D registration algorithms to overcome both th ..."
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Cited by 9 (3 self)
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Abstract — An iterative temporal registration algorithm is presented in this paper 1 for registering 3D range images obtained from unmanned ground and aerial vehicles traversing unstructured environments. We are primarily motivated by the development of 3D registration algorithms to overcome both the unavailability and unreliability of Global Positioning System (GPS) within required accuracy bounds for Unmanned Ground Vehicle (UGV) navigation. After suitable modifications to the wellknown Iterative Closest Point (ICP) algorithm, the modified algorithm is shown to be robust to outliers and false matches during the registration of successive range images obtained from a scanning LADAR rangefinder on the UGV. Towards registering LADAR images from the UGV with those from an Unmanned Aerial Vehicle (UAV) that flies over the
A neartight approximation algorithm for the robot localization problem
 SIAM
"... Abstract. Localization is a fundamental problem in robotics. The “kidnapped robot ” possesses a compass and map of its environment; it must determine its location at a minimum cost of travel distance. The problem is NPhard [G. Dudek, K. Romanik, and S. Whitesides, SIAM J. Comput., 27 (1998), pp. 58 ..."
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Cited by 7 (0 self)
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Abstract. Localization is a fundamental problem in robotics. The “kidnapped robot ” possesses a compass and map of its environment; it must determine its location at a minimum cost of travel distance. The problem is NPhard [G. Dudek, K. Romanik, and S. Whitesides, SIAM J. Comput., 27 (1998), pp. 583–604] even to minimize within factor c logn [C. Tovey and S. Koenig, Proceedings