Abstract: In applications such as vacuum cleaning, painting, demining and foraging, a mobile robot must cover an unknown surface. The efficiency and completeness of coverage is improved via the construction of a map of covered regions while the robot covers the surface. Existing methods generally use grid maps, which are susceptible to odometry error and may require considerable memory and computation. This paper proposes a topological map and presents a coverage algorithm in which natural landmarks are added as nodes in a partial map. The completeness of the algorithm is argued. Simulation tests show over 99% of the surface is covered; 85% for real (Khepera) robot tests. The path length is about 10% worse than optimal in simulation tests, and about 20% worse than optimal for the real robot, which are within theoretical upper bounds for approximates solutions to traveling salesman based coverage problems. The proposed algorithm generates shorter paths and covers a wider variety of environments than topological coverage based on Morse decompositions.

Abstract: This paper presents both the theory and the first experimental results of a new method which allows simultaneous estimation of the robot configuration and the odometry error (both systematic and non-systematic) during the mobile robot navigation. The estimation of the non-systematic components is carried out through an augmented Kalman filter which estimates a state containing the robot configuration and the parameters of the odometry error. It uses encoder readings as inputs and the readings from a laser range finder as observations. The estimation of the non-systematic components is carried out through another Kalman filter where the observations are obtained by two subsequent robot configurations provided by the previous augmented Kalman filter.

Abstract: An advanced sonar sensor produces accurate range and bearing measurements, classifies targets and rejects interference with one sensing cycle. Two advanced sonar systems are used to simultaneously localise and map an indoor environment using a mobile robot. This paper presents the approach and results from on-the-fly map building using a Kalman filter and a new odometry error model that incorporates variations in effective wheel separation and angle measurements. This model is suited to pneumatic tyre odometry errors where the wheel separation has been found to vary unpredictably with floor surface and path curvature. The paper also presents techniques for detecting sonar feature clutter and selecting strong candidates for ultrasonic landmarks. The paper illustrates that sonar SLAM data association problems are significantly simplified when advanced sonar sensors are employed compared to Polaroid ranging modules.

Abstract: All mobile robots suffer from odometry error. Relative localization from odometry has both the systematic and the non-systematic errors. However, once a precise system error model and its parameters are given, the accuracy of odometry can be remarkably improved. Most previous works on this effort focused on the differential drive robots with little attention to the other types of mobile bases. In this paper, we analyze sources of odometry error and propose an error model for the synchro drive robot. We then describe a novel procedure to accurately estimate the error parameters of the derived error model and the covariance matrix of the synchro drive robot. However, this procedure is general for all mobile bases, so we also apply our method for the differential drive robots and show experiments. This new process uses the shape of the path, as opposed to just end points, to estimate the error parameters and covariance matrix. We happen to use the generalized Voronoi graph to generate this path. Experimental results validate the error model of the synchro drive robot and precise estimation ability of the proposed method for the synchro and the differential drive robots.

Abstract: This paper addresses the problem of the odometry error estimation during the robot navigation. The robot is equipped with an external sensor (like laser range finder). Concerning the systematic error an augmented Kalman Filter is introduced. This filter estimates a vector state containing the robot configuration and the parameters characterizing the systematic component of the odometry error. It uses encoder readings as inputs and the readings from the external sensor as observations. The estimation of the non-systematic component is carried out through another Kalman Filter where the observations are obtained by two subsequent robot configurations provided by the previous augmented Kalman Filter. Both synchronous and differential drive systems are considered.

Abstract: A spatial data collection apparatus collects and correlates spatial data for use in creating floor plans, maps and models of existing spaces The apparatus includes a mobile platform with wheels to allow movement around a space One or more positional sensors on the platform generate positional data related to a position of the platform The positional data can include odometry data and optionally gyroscopic data for correcting the odometry data One or more range-finding devices on the platform measure and calculate range data (eg, 2-D or 3-D) relating to distances and angles between the platform and objects in the space The apparatus collects, fuses and correlates the positional data and range data to produce spatial data and stores the spatial data for transfer to a PC for use in creating, viewing and/or editing a map of the space The apparatus can also record media files (eg, audio, video, or images) to be associated with locations within the map

Abstract: An apparatus and method of recognizing a position and direction of a mobile robot includes obtaining absolute coordinates at a current position of the mobile robot and relative coordinates for a moving displacement of the mobile robot. Therefore, the position and direction of the mobile robot are recognized by reflecting the relative coordinates on the absolute coordinates. Accordingly, the present invention operates odometry and RFID coordinate systems in combination with each other, thus obtaining a high sampling rate by the odometry coordinate system while restricting an error range within a predetermined level by the RFID coordinate system.

Abstract: This thesis has two main contributions. The first contribution is the use of cooperative localization for decoupling the positional error of a moving robot from its environment. The second contribution is the development of efficient multi-robot exploration strategies for an unknown environment. The proposed method is designed to be robust in the face of arbitrarily large odometry errors or objects with poor reflectance characteristics. Central to the exploration strategy is a sensor (robot tracker) mounted on a robot that could track a second mobile robot and accurately report its relative position. Our exploration strategies use the robot tracker sensor to sweep areas of free space between stationary and moving robots and to generate a graph-based description of the environment. This graph is used to guide the exploration process. Depending on the size of the environment relative to the range of the robot tracker, different spatial decompositions are used: a triangulation or a trapezoidal decomposition of the free space. Complete exploration without any overlaps is guaranteed as a result of the guidance provided by the dual graph of the spatial decomposition of the environment. The uncertainty in absolute robot positions and the resulting uncertainty in the map is reduced through the use of a probabilistic framework based on particle filtering (a Monte Carlo simulation technique). Particle filtering is a probabilistic sampling technique used to efficiently model complex probability distributions that cannot be effectively described using classical methods (such as Kalman filters). We present experimental results from two different implementations of the robot tracker sensor, in simulated and in real environments. The accuracy of the resulting map increases with the use of cooperative localization. Furthermore, the deterioration of the floor conditions did not affect the quality of the map verifying the decoupling of positioning error from the environment.

Abstract: This paper describes the implementation of an autonomous navigation system onto a 30 tonne Load-Haul-Dump truck. The control architecture is based on a robust reactive wall-following behaviour. To make it purposeful we provide driving hints derived from an approximate nodal-map. For most of the time, the vehicle is driven with weak localization (odometry). This need only be improved at intersections where decisions must be made-a technique we refer to as opportunistic localization. The truck has achieved full-speed autonomous operation at an artificial test mine, and subsequently, at a operational underground mine.

Abstract: This paper addresses the problem of localization and map construction by a mobile robot in an indoor environment. Instead of trying to build high-fidelity geometric maps, we focus on constructing topological maps, as they are lees sensitive to poor odometry estimates and position errors. We propose a method for incrementally building topological maps for a robot, which uses a panoramic camera to obtain images at various locations along its path and uses the features it tracks in the images to update the topological map. The method is very general and does not require the environment to have uniquely distinctive features.

Abstract: This article presents an approach to improve and monitor the behavior of a skid-steering rover on rough terrains. An adaptive locomotion control generates speeds references to avoid slipping situations. An enhanced odometry provides a better estimation of the distance travelled. A probabilistic classification procedure provides an evaluation of the locomotion efficiency on-line, with a detection of locomotion faults. Results obtained with a Marsokhod rover are presented throughout the paper.

Abstract: This paper addresses the problem of computing the trajectoryof a camera from sparse positional measurementsthat have been obtained from visual localisation, and densedifferential measurements from odometry or inertial sensors.A fast method is presented for fusing these two sourcesof information to obtain the maximum a posteriori estimateof the trajectory. A formalism is introduced for representingprobability density functions over Euclidean transformations,and it is shown how these density functions can bepropagated along the data sequence and how multiple estimatesof a transformation can be combined. A three-passalgorithm is described which makes use of these results toyield the trajectory of the camera.Simulation results are presented which are validatedagainst a physical analogue of the vision problem, and resultsare then shown from sequences of approximately 1,800frames captured from a video camera mounted on a go-kart.Several of these frames are processed using computer visionto obtain estimates of the position of the go-kart. The algorithmfuses these estimates with odometry from the entiresequence in 150 mS to obtain the trajectory of the kart.

Abstract: This paper presents a method of building a structured map, which consists of objects such as furniture. We represent a map as a graph, in which a node represents an object and an arc represents a relative pose between objects. The robot localizes itself and builds a map using odometry readings and sensor data obtained by object recognition. To correct the map distortion caused by errors in the data, the robot utilizes loops as geometric constraints, and imports geometric knowledge provided by a hand-made map. Experiments show that the root successfully built a map of corridors, and a map of a room having many objects.

Abstract: This paper addresses the problem of computing the trajectory of a camera from sparse positional measurements that have been obtained from visual localisation, and dense differential measurements from odometry or inertial sensors A fast method is presented for fusing these two sources of information to obtain the maximum a posteriori estimate of the trajectory A formalism is introduced for representing probability density functions over Euclidean transformations, and it is shown how these density functions can be propagated along the data sequence and how multiple estimates of a transformation can be combined A three-pass algorithm is described which makes use of these results to yield the trajectory of the camera Simulation results are presented which are validated against a physical analogue of the vision problem, and results are then shown from sequences of approximately 1,800 frames captured from a video camera mounted on a go-kart Several of these frames are processed using computer vision to obtain estimates of the position of the go-kart The algorithm fuses these estimates with odometry from the entire sequence in 150 ms to obtain the trajectory of the kart

Abstract: Up to recently autonomous mobile robots were mostly designed to run within an indoor, yet partly structured and flat, environment. In rough terrain many problems arise and position tracking becomes more difficult. The robot has to deal with wheel slippage and large orientation changes. In this paper we will first present the recent developments on the off-road rover Shrimp. Then a new method, called 3D-Odometry, which extends the standard 2D odometry to the 3D space will be developed. Since it accounts for transitions, the 3D-Odometry provides better position estimates. It will certainly help to go towards real 3D navigation for outdoor robots.

Abstract: By the extrapolation of movement increments detected by differential encoders, the position of a mobile robot can be easily computed. However, the encoders suffer from various systematic errors resulting in an increasing error of the obtained robot position. This problem is also known from the scope of inertial navigation, but the transfer of the respective concepts of maintaining merely errors to mobile robot localization has, with few exceptions, been neglected so far. In this paper, the position error is related to the encoder errors in an augmented state-space system. As done in inertial guidance, the position error is estimated by an error-state Kalman filter making use of general complementary sensor information as, for example, absolute position measurements or redundantly detected movement increments. Specifically, the usage of a rate gyroscope as a complementary sensor is examined. This case is somehow special, as it implies a correlation of the overall error model and the observation, demanding ...

Abstract: This work describes an investigation to reduce positioning error of 3 wheel middle size robot by using a modified odometry system. In this technique the positioning sensor (shaft encoders) are mounted on 3 free-running wheels so the slippage of the driving wheels does not affect the measurements of the sen- sors. This will result in decreasing the cumulative error of the system. This mechanism accompanying by omnidirectional vision system presents reliable and accurate self-localization method for any 3 wheel driving robot. Ex- perimental results have shown performance improvement up to 86% in orienta- tion error and 80% in position error.

Abstract: This paper addresses the problem of simultaneous localization and mapping (SLAM) for the case of very small, resource-limited robots which have poor odometry and can typically only carry a single monocular camera. We propose a modification to the standard SLAM algorithm in which the assumption that the robots can obtain metric distance/bearing information to landmarks is relaxed. Instead, the robot registers a distinctive sensor "signature", based on its current location, which is used to match robot positions. In our formulation of this non-linear estimation problem, we infer implicit position measurements from an image recognition algorithm. The iterated form of the extended Kalman filter (IEKF) is employed to process all measurements.

Abstract: In this paper a hierarchical multi-resolution approach allowing for high precision and distinctiveness is presented. The method combines topological and metric paradigm. The metric approach, based on the Kalman Filter, uses a new concept to avoid the problem of the drift in odometry. For the topological framework the fingerprint sequence approach is used. During the construction of the topological map, a communication between the two paradigms is established. The fingerprint used for topological navigation enables also the re-initialization of the metric localization. The experimentation section will validate the multi-resolution-representation maps approach and presents different steps of the method.

Abstract: This paper presents an efficient and robust method for registration of terrain models created using stereovision on a planetary rover. Our approach projects two surface models into a virtual depth map, rendering the models, as they would be seen from a single range sensor. Correspondence is established based on which points project to the same location in the virtual range sensor. A robust norm of the deviations in observed depth is used as the objective function, and the algorithm searches for the rigid transformation, which minimizes the norm. An initial coarse search is done using rover pose information from odometry and orientation sensing. A fine search is done using Levenberg-Marquardt. Our method enables a planetary rover to keep track of designated science targets as it moves, and to hand off targets from one set of stereo cameras to another. These capabilities are essential for the rover to autonomously approach a science target and place an instrument in contact in a single command cycle.

Abstract: A method for odometry calibration of unicycle-like mobile robots is presented in this paper. The equations of motion are written so as to exploit linearity in a proper set of unknown parameters and the least-squares technique is then applied to estimate them. Experimental results show the effectiveness of the proposed technique in reducing the systematic odometric errors.

Abstract: A localization system using GPS, ABS sensors and a driving wheel encoder is described and tested through real experiments. A new odometric technique using the four ABS sensors is presented. Due to the redundancy of measurements, the precision is better than the method using differential odometry on the rear wheels only. The sampling is performed when necessary and when a GPS measurement is performed. This implies a noticeable reduction of the GPS latency, simplifying the data-fusion process and improving the quality of its results.

Abstract: One difficult problem in robotics is localization: the ability of a mobile robot to determine its position in the environment. Roboticists around the globe have been working to find a solution to localization for more than 20 years; however, only in the past 4‐5 years we have seen some promising results. In this work, we describe a first‐of‐a‐kind, breakthrough technology for localization that requires only one low‐cost camera (less than 50USD) and odometry to provide localization. Because of its low‐cost and robust performance in realistic environments, this technology is particularly well‐suited for use in consumer and commercial applications.

Abstract: We discuss our experiences in integrating a commercial off-the-shelf ground-penetrating radar unit with an all-terrain rover. Straight-line subsurface surveys were generated in a fully autonomous manner using odometry and a simple visual servoing technique. Survey results for various terrains are presented. We discuss the configuration of the integrated system and make recommendations for both Martian and terrestrial applications.

Abstract: In this paper we describe a method for localizing and tracking a mobile robot in the type of field used for RoboCup mid-size soccer robots. Each robot carries an omnidirectional camera which provides a 360 degree view of the robot’s surroundings. The only features that we use for localizing the robot are white lines present on the green carpet. These white lines are easy to find and discriminate – they are not too dependent on the color calibration of the camera. The MATRIX method localizes a robot in a field by testing a grid of hypothetical positions for a best fit to the data. Once localized, tracking the robot from frame to frame, in real time, can be done by combining the odometric data with the MATRIX pattern matching. 1 Mobile Robots Localization Localizing mobile robots in a known environment is easier that localizing robots in a space that has to be explored and mapped first. However, if the sensors used by the robots are noisy or if the computational capabilities of the robot are limited, localizing a robot in a known environment can also be a hard problem. This is the case in the RoboCup environment, where mobile robots compete trying to be as fast, yet as precise as possible. Usually, the robots have only a partial view of the field and color calibration tends to be difficult, due to varying lightning conditions. Also, spectators sitting or standing around the field, can be mistakenly identified with the yellow and blue poles used at the corners of the field. Therefore extensive color calibration is done by all teams before a game starts. Some groups have studied methods that could avoid work even without color information, for example, for finding the ball, but they are computationally expensive and not feasible yet [8]. It would be important to be able to depend less on the color calibration of the cameras and more on certain prominent features of the field. The white lines on the green carpet are a very good example, since they are specially prominent and easy to find. Some RoboCup teams already exploit such features in order to localize the robot. Utz et al. ([9]) work with this kind of information, but map it to Hough space which provides a kind of assessment of the number of expected features visible on the field. It is a form of pattern matching, using the information available in Hough spaces for lines and circles. Nevertheless, it is very difficult to leave the localization of a mobile soccer robot alone to the vision system. If the robot gets confusing images (because, for example, another robot is obstructing the camera), then the robot loses its initial position which can be difficult to recompute again – the robot gets lost on the field. Therefore, we need a method for localizing the robot globally on the field, and another method for tracking the robot from one known position to the next [7]. Odometry is a straightforward method for disambiguating vision data. Human soccer players do not look at the field constantly, they have a “feeling” for their current position extrapolated from the last known position. They know in which direction they have moved, and for how long, and so they only need to look occasionally to the field, in order to “recalibrate” their current position. This is also the best strategy for a mobile robot, specially since odometric data tends to be much more precise than data for legged robots. Our mid-size robots can drive forward for about three meters, and the localization error after stopping is just several centimeters (just how many, depends on the velocity and direction of the robot). A combination of odometric with vision data can reduce the load on the control computer enormously. Instead of having to process each frame of a video in search of field features, it would be possible to track the robot during short periods based fully on the reported odometry, while the vision can recalibrate the odometric data periodically, for example once a second. Then only one frame every second has to be fully processed for determining the robot’s position, while the odometry is reaching the robot in a continuous stream. Of course, for finding the ball and going for it, it is important to process 30 fps, but only in order to find the orange blob inside the field. Since our localization method is so fast, we can even use it to correct the robot position in every frame. One problem with odometry data is that, in the case of omnidirectional robots, three wheels produce the desired movement, sometimes by working against each other. The wheels should not slip on the floor, and if they do, the odometry data has to be corrected to take this slippage into account. In this paper we present a method to correct the odometry data. 2 Localization in a flat featured environment A flat featured environment is one in which lines on the otherwise featureless floor provide position information. This is the case of the RoboCup environment, in which white lines on a green carpet delineate a soccer field of up to 10 meters long by 5 meters wide. We would like to localize the mobile robot using this information. Fig. ?? shows a picture of the field and the markers used. One goal box is blue, the other yellow. The poles at the four corners are painted yellow and blue. The frame captured by an omnidirectional camera is processed by our mobile robot, which tracks regions in the field using an algorithm described elsewhere (cite). The robot then looks for color transitions between green and white, and dark and white. This color transitions represent possible lines on the field, that we want to identify and track. Fig. 1 shows a cloud of points, from the perspective of the robot. The dark spots represent edges between white and green colors, or viceversa. It is easy for a human to match this cloud to a model of the field, but it has to be done automatically by the robot and in only a few milliseconds. −300 −200 −100 0 100 200 300 −200 −150 −100 −50 0 50 100 150 200 250 300 Fig. 1. A cloud of vision data points representing white lines on the field, from the perspective of the robot 2.1 Global localization If the robot does not have its initial position, we use a pattern matching approach to find the best possible hypothesis. The idea is the following. If we have the cloud of vision points, we can rotate it several times (for example 16 times) and then we can translate each rotated cloud to each of several hypothetical regions of space (see Fig. 2, left). These position hypotheses form a grid on one half of the field (Fig. 2, right). We only use one half of the field, because the other half is symmetrical. The robot can be localized on one side or the other. Additional information has to be used to disambiguate the choice (like the color of the box goals seen). Given the rotation and centering of the cloud, the quality of the match to the field is measured by computing, for each point, the distance to the nearest segment or circle in the model of the field. These distances are added for all points, are averaged, and the final number represents the quality of the match. To speed up the computation, we compute beforehand a matrix of distances of every element in a 100 by 70 grid to the segments and circles in the model. Given the cloud of points it then is easy to do a table look-up to associate each point with the distance to the nearest segment. A cloud of points has typically 500 to 700 points, so that the table lookup and averaging takes only a few microseconds. −300 −200 −100 0 100 200 300 −300 −200 −100 0 100 200 300 −300 −200 −100 0 100 200 300 −250 −200 −150 −100 −50 0 50 100 150 200 250 Fig. 2. The points on the field represent the hypothesis tested for global localization. 16 orientations are tested for each position. Fig. 3 shows the matrix of “qualities, that is, the distance vector from each point in a grid that covers the field (and also points outside the field) to the nearest model element. Ideally, we would like to compute the quadratic error associated with a least squares match of the cloud point to the model. However, the minimal distance function is not smooth: midway between two parallel line segments the distance vector changes direction suddenly. It is then difficult to provide an analytic model of the field, that does not contain “if” decisions. 2.2 Tracking the robot Tracking the robot can be done also by using pattern matching. The idea is to start from a known position and orientation of the robot. We rotate the cloud of points by the known orientation and translate the cloud to the known position. The cloud should be a good match of the model. If the robot moves, the next cloud from the camera (repositioned and reoriented as before) will almost match the model, but will have moved. The translation and rotation of the cloud with respect to the model is the translation and rotation of the robot. We need to compute the inverse transformation, in order to recenter the new cloud and make it fit the model as well as possible. The inverse transformation is computed iteratively. We start from the new cloud of points and compute the total average “force” on each point. The force on a point is the attraction that a segment or circle of the model exert on each point. The force is proportional to the distance to each line element. We add all forces acting on a point, but weighting them in a way that ensures that the nearest line elements have a greater contribution to the estimated force. If (x, y) are the coordinates of a point and the array `i, i = 1, . . . , 11. represents its distances to the 11 model elements, the weight wi for each single −400 −300 −200 −100 0 100 200 300 400 −400 −300 −200 −100 0 100 200 300 400

Abstract: This paper describes a complete approach for localization and path selection of any vehicle like a mobile robot to achieve any planetary exploration missions In this study, localization process was realized on a mobile robot, Nomad 200, using its odometry and gyro measurements These sensors information was integrated, by extended Kalman Filter (EKF) algorithm On the other hand, on a given planetary surface, which can be obtained by using many separated cameras or using backpropagated beacons, a lot of safely traveled paths are formed due to the vehicle mission A path selection was constructed due to its cost function among the selected paths In this part, vehicle is forced to select a proper path to reach its target This selection is performed using cost function of these paths Cost values are strictly bounded to the surface of the planet Only simulation results of this section were given We believe that this path selection study presented here will satisfy valuable results on a real robotic vehicle that is suitable to navigate a planet like a rover

Abstract: In order for a robot to follow a predetermined trajectory accurately, its position must be estimated accurately and reliably. This paper proposes a trajectory tracking control method for a nonholonomic mobile robot that utilizes both iGPS and odometry localization information to precisely calculate the robot's position. The iGPS is an indoor global positioning system that utilizes TV cameras mounted on the ceiling to locate the robot with respect to the world coordinate system. The iGPS localization information is sent to the robot via a wireless LAN, and the robot estimates its position from this information and the robot's internal odometry. The experimental results show that the proposed method enables robust trajectory tracking, even after experiencing a significant disturbance.