Abstract: In this paper, we present a framework and the software architecture for the deployment of multiple autonomous robots in an unstructured and unknown environment, with applications ranging from scouting and reconnaissance, to search and rescue, to manipulation tasks, to cooperative localization and mapping, and formation control. Our software framework allows a modular and hierarchical approach to programming deliberative and reactive behaviors in autonomous operation. Formal definitions for sequential composition, hierarchical composition, and parallel composition allow the bottom-up development of complex software systems. We demonstrate the algorithms and software on an experimental testbed that involves a group of carlike robots, each using a single omnidirectional camera as a sensor without explicit use of odometry.

Abstract: This paper addresses the problem of large scale multiview registration of range images captured from unknown viewing directions. To reduce the computational burden, we decouple the local problem of pairwise registration on neighboring views from the global problem of distribution of accumulated errors. We define the global problem over the graph of neighboring views, and we show that this graph can be decomposed into a set of cycles such that the optimal transformation parameters for each cycle can be solved in closed form. We then describe an iterative procedure that can be used to integrate the solutions for the set of cycles across the graph. This method for error distribution does not require point correspondences between views, and therefore can be used together with robot odometry or any method of pairwise registration. Experimental results demonstrate the effectiveness of this technique on range images of an indoor facility.

Abstract: We present a vision-based approach to mobile robot localization, that integrates an image retrieval system with Monte-Carlo localization. The image retrieval process is based on features that are invariant with respect to image translations, rotations, and limited scale. Using the local features the system is robust against distortion and occlusions, which is especially important in populated environments. By using the sample-based Monte-Carlo localization technique our robot is able to globally localize itself to reliably keep tracking of its position, and to recover from localization failures. Both techniques are combined by extracting for each image a set of possible view-points using a two-dimensional map of the environment. Our technique was implemented and tested extensively. We present several experiments demonstrating the reliability and robustness of our approach even in the context of dynamics in the environment and larger errors in the odometry.

Abstract: To navigate in unknown environments, mobile robots require the ability to build their own maps. A major problem for robot map building is that odometry-based dead reckoning cannot be used to assign accurate global position information to a map because of cumulative drift errors. This paper introduces a fast, on-line algorithm for learning geometrically consistent maps using only local metric information. The algorithm works by using a relaxation technique to minimize an energy function over many small steps. The approach differs from previous work in that it is computationally cheap, easy to implement and is proven to converge to a globally optimal solution. Experiments are presented in which large, complex environments were successfully mapped by a real robot.

Abstract: Tracked mobile robots in the 20 kg size class are under development for applications in urban reconnaissance. For efficient deployment, it is desirable for teams of robots to be able to automatically execute path following behaviors, with one or more followers tracking the path taken by a leader. The key challenges to enabling such a capability are (1) to develop sensor packages for such small robots that can accurately determine the path of the leader and (2) to develop path following algorithms for the subsequent robots. To date, we have integrated gyros, accelerometers, compass/inclinometers, odometry, and differential GPS into an effective sensing package. The paper describes the sensor package, sensor processing algorithm and path tracking algorithm we have developed for the leader/follower problem in small robots and shows the results of performance characterization of the system. We also document pragmatic lessons learned about design, construction, and electromagnetic interference issues particular to the performance of state sensors on small robots.

Abstract: We describe a method to recognize moving obstacles in a wide view and in real-time. Such an ability is required when a mobilerobot moves in a dynamic environment. Our method uses an omnidirectional stereo vision which is composed of a pair ofvertically-aligned omnidirectional cameras and a PC cluster to obtain panoramic range information of 360 degrees in real-time.From this range information, the robot on-line generates a free space map of the surrounding environment, and extracts objectsin the free space as candidates for moving obstacles. The robot makes temporal correspondence of the candidates and estimatestheir position and velocity using the Kalman filter. To reduce the effect of odometry error to map generation, the ego-motionis estimated by comparing the current and previous range data. We demonstrate the effectiveness of our method by on-lineexperiments.

Abstract: In this paper we present a new probabilistic feature-based approach to multi-hypothesis global localization and pose tracking. Hypotheses are generated using a constraint-based search in the interpretation tree of possible local-to-global pairings. This results in a set of robot location hypotheses of unbounded accuracy. For tracking, the same constraint-based technique is used. It performs track splitting as soon as location ambiguities arise from uncertainties and sensing. This yields a very robust localization technique which can deal with significant errors from odometry, collisions and kidnapping. Simulation experiments and first tests with a real robot demonstrate these properties at very low computational cost. The presented approach is theoretically sound which makes that the only parameter is the significance level on which all statistical decisions are taken.

Abstract: In this paper we present a new probabilistic feature-based approach to multi-hypothesis global localization and pose tracking. Hypotheses are generated using a constraintbased search in the interpretation tree of possible localto-global pairings. This results in a set of robot location hypotheses of unbounded accuracy. For tracking, the same constraint-based technique is used. It performs track splitting as soon as location ambiguities arise from uncertainties and sensing. This yields a very robust localization technique which can deal with significant errors from odometry, collisions and kidnapping. Simulation experiments and first tests with a real robot demonstrate these properties at very low computational cost. The presented approach is theoretically sound which makes that the only parameter is the significance level on which all statistical decisions are taken.

Abstract: We show how to recover 2D structure and motion linearly in order to initialize simultaneous mapping and localization (SLAM) for bearings-only measurements and planar motion. The method supplies a good initial estimate of the geometry, even without odometry or in multiple robot scenarios. Hence, it substantially enlarges the scope in which non-linear batch-type SLAM algorithms can be applied. The method is applicable when at least seven landmarks are seen from three different vantage points, whether by one robot that moves over time or by multiple robots that observe a set of common landmarks.

Abstract: Proposes an indoor navigation system for an autonomous mobile robot including the teaching of its environment. The self-localization of the vehicle is done by detecting the pose of corridor fluorescent tubes with a camera pointing to the ceiling. A map of the lights based on odometry and used later for navigation tasks is first built by remotely controlling the robot. The map distortion due to positioning errors is corrected to facilitate route definition by detecting and closing open cycles extremities. Paths defined on the modified map whose geometry differs from the robot one are then converted automatically into the robot map so that the robot can refer to it and cancel odometry errors during navigation.

Abstract: This paper presents an algorithm for simultaneous localization and mapping for a mobile robot using monocular vision and odometry. The approach uses variable state dimension filtering (VSDF) framework to combine aspects of extended Kalman filtering (EKF) and nonlinear batch optimization. This paper describes two primary improvements to the VSDF. The first is to use the maximally informative statistics criterion to derive an interpolation scheme for linearization in recursive filtering. The interpolation is based on fitting a set of deterministic samples rather than using analytic Jacobians. The second improvement is to replace the inverse covariance matrix-in the VSDF with its Cholesky factor to improve the computational complexity. Results of applying the filter to the localization and mapping are presented.

Abstract: This paper addresses the problem of mobile robot localization in urban environments. Typically, GPS is the preferred sensor for outdoor operation. However, using GPS-only localization methods leads to significant performance degradation in urban areas where nearby tall structures obstruct the clear view of the satellites. In our work, we use vision-based techniques to supplement GPS and odometry and provide accurate localization. The vision system identifies prominent linear features in the scene and matches them with a reduced model of nearby buildings, yielding improved pose estimation of the robot.

Abstract: In this paper the odometry error of a mobile robot with a synchronous drive system is modeled by introducing four parameters characterizing its systematic and non-systematic components (translational and rotational). A strategy in order to simultaneously estimate the model parameters is suggested. This strategy only requires to measure the change in the orientation and in the position between the initial and the final configuration of the robot related to suitable robot motions. In other words, it is unnecessary to know the actual path followed by the robot. The proposed strategy is illustrated by discussing the accuracy on the parameter estimation both in an indoor and outdoor environment.

Abstract: We describe two robotic systems for acquiring high-resolution volumetric maps of underground mines. Our systems have been deployed in an operational coal mine in Bruceton, Pennsylvania, where they have been used to generate interactive 3-D maps. Our approach includes a novel sensor head, assembled from multiple SICK laser range finders, and a real-time algorithm for scan matching that generates accurate volumetric maps. The scan matching algorithm performs horizontal and vertical simultaneous localization and mapping (SLAM). Data from the horizontal scans is used to remove artifacts in the vertical scans, and vice versa. The system can construct full 3-D volumetric maps hundreds of meters in diameter, even when no odometry information is available.

Abstract: When a mobile robot is constructed the odometry must be calibrated. Calibrating a 4-wheel robot requires the accurate measurement of steering angle as well as translation and rotation. Some measurements are made with a tape measure and some with ultrasonic sensors. The measurements are used to determine the parameters of the odometry calibration matrix and the steering kinematic model. A procedure for calibrating a 4-wheel robot is discussed.

Abstract: The odometry error of a mobile robot contains both sys- tematic and non-systematic components. The first ones are independent of the environment while the second ones depend on the interaction of the robot with the environ- ment where the robot moves. In this paper the odometry error of a mobile robot with a synchronous drive system is modeled by introducing four parameters characterizing its systematic and non- systematic components (translational and rotational). Some experimentally measurable quantities, called ob- servables, are introduced for a given robot motion. On the basis of the odometry error model the average values and the variances of the observables are analytically com- puted. These quantities depend on the previous model parameters and on the considered robot motion. A strat- egy in order to simultaneously estimate the model pa- rameters by estimating the observables is suggested. This strategy only requires to measure the change in the ori- entation and in the position between the initial and the final configuration of the robot related to suitable robot motions. In other words it is unnecessary to know the ac- tual path followed by the robot. The proposed strategy is illustrated by discussing the accuracy on the parameters estimation both in an indoor and outdoor environment.

Abstract: Navigating autonomously in a domestic environment is a problem that has attracted a great deal of interest in mobile robotics. A robotic system that operates in ordinary furnished rooms without the need of an engineered environment has many different applications such as service, cleaning and surveillance tasks or simply entertainment. Robotic systems that use artificial landmarks or pre-stored maps of the environment are available today. However, these systems are not very flexible. The user must in fact supply a map of the environment, which can be interpreted by the system. This thesis deals with the problem of Simultaneous Localization and Mapping (SLAM). The mobile robot builds a map of an unexplored environment while simultaneously using this map to localize itself. The feature based approach used in this thesis utilizes the Extended Kalman Filter (EKF) machinery to estimate the pose of the robot and the location of the features. This approach is referred to as stochastic mapping. Point features in the environment are robustly extracted from sonar data using triangulation techniques. In addition, this thesis explores a method for recovering from the most common mode of failure of the stochastic mapping approach. This method allows the EKF algorithm to continue in a consistent manner after a failure has been detected. Finally, the thesis presents a method for achieving more accurate navigation by using the architectural properties of most domestic environments. This method drastically improves navigation, when the stochastic mapping algorithm can not be used due to poor quality sensor data. All the algorithms presented in this thesis have been tested and verified in real world experiments.

Abstract: This paper presents results from a four transducer pulse coded sonar system that performs target localisation in two dimensions and classification into planes, concave corners and convex edges whilst the sensor is in motion. On each sensing cycle two pulses are emitted from separate transmitters, and two receivers collect echoes that are processed using a DSP. The sensor achieves on-the-fly classification by transmitting nearly and simultaneously from the two transmitters. The effects of sensor motion are analysed in the paper and effects on range and bearing estimation can then be compensated using odometry based robot velocity measurements. Results are presented that show classification and angle measurement deviations from a robot moving at speeds up to 1 metre per second.

Abstract: This paper focuses on issues of odometry for the special case of synchronous drive wheeled mobile robots. In particular, the uncertainty in odometry is modeled by a four parameter statistical model already introduced in previous works together with a strategy to estimate the four parameters from actual data obtained from a given mobile robot. To obtain the data, the robot traverses a path. The error realized along that path is then used to estimate the model parameters. The accuracy on the parameter estimation through this strategy is here discussed by considering paths different for length and shape.

Abstract: This work introduces a method for robot navigation in structured indoors environments, based on the information of multiple sensors. Guidance control is based on odometry, reset at some time instants by a vision-based self-localization algorithm introduced in previous work. Sonar data is used to avoid and go around obstacles. Results from the application of the complete navigation system to a real robot moving on a RoboCup soccer field are presented.

Abstract: We describe a method for the reconstruction of a driven trajectory of a mobile robot if the begin and end states of the trajectory are known, and intermediate readings from odometry are available. Our method uses a Kalman filter to combine a forward and backward dead-reckoning trajectory. We show that our method is more reliable for long trajectories than just combining the dead-reckoning trajectories independently.

Abstract: : The authors describe two robotic systems for acquiring high-resolution volumetric maps of underground mines. Their systems have been deployed in an operational coal mine in Bruceton, Pennsylvania, where they have been used to generate interactive 3-dimensional maps. Their approach includes a novel sensor head assembled from multiple SICK laser range finders, and a real-time algorithm for scan matching that generates accurate volumetric maps. The scan matching algorithm performs horizontal and vertical simultaneous localization and mapping (SLAM). Data from the horizontal scans is used to remove artifacts in the vertical scans, and vice versa. The system can construct full 3-dimensional volumetric maps hundreds of meters in diameter, even when no odometry information is available.

Abstract: This paper focuses on issues of odometry for the special case of synchronous drive wheeled mobile robots. In particular, the uncertainty in odometry is modeled by a four parameter statistical model already introduced in previous works together with a strategy to estimate the four parameters from actual data obtained from a given mobile robot. To obtain the data, the robot traverses a path. The error realized along that path is then used to estimate the model parameters. The accuracy on the parameter estimation through this strategy is here discussed by considering paths different for length and shape.

Abstract: This paper proposes an indoor navigation system for an autonomous mobile robot including the teaching of its environment. The self-localization of the vehicle is done by detecting the position and orientation of fluorescent tubes located above it’s desired path thanks to a camera pointing to the ceiling. A map of the lights based on odometry data is built in advance by the robot guided by an operator. Then a graphic user interface is used to define the trajectory the robot must follow with respect to the lights. While the robot is moving, the position and orientation of the lights it detects are compared to the map values, which enables the vehicle to cancel odometry errors.

Abstract: Dead reckoning is an important method for mobile robot. If its accuracy can be improved, navigation tasks will be simplified. The mobile robot concerned in this paper is with five wheels, in which there are dual driving wheels and a castor. A measurement wheel with encoder is fixed beside each driving wheel. There are two encoders fixed on the castor. One measures the castor's movement, and another indicates the angle of yawing. A new kind of kinematics equations derived from the robot's turning radius and angle of movement trajectory is presented. We can get a group of turning radius and angle from the data of four encoders, in which there are four different values for radius and six different values for angle. The estimated values of radius and angle can be gotten by fusion using fuzzy algorithm. The improved dead reckoning method has the advantages of simplicity, less computation cost, cheapness and good performance in uneven road. The simulation shows its effectiveness.

Abstract: This paper presents a planner that determines a path such that the robot does not have to heavily rely on odometry to reach its goal. The planner determines a sequence of obstacle boundaries that the robot must follow to reach the goal. Since this planner is used in the context of a coverage algorithm already presented by the authors, we assume that the free space is already, completely or partially, represented by a cellular decomposition whose cell boundaries are defined by critical points of Morse functions (isolated points at obstacle boundaries). The topological relationship among the cells is represented by a graph where nodes are the critical points and edges connect the nodes that define a common cell (i.e., the edges correspond to the cells themselves). A search of this graph yields a sequence of cells that directs the robot from a start to a goal. Once a sequence of cells and critical points are determined, a robot traverses each cell by mainly following the boundary of the cell along the obstacle boundaries and minimizes the accumulated dead-reckoning error at the intermediate critical points. This allows the robot to reach the goal robustly even in the presence of dead-reckoning error.

Abstract: This paper describes a method for localizing the members of a mobile robot team by using only the robots themselves as landmarks. That is, we describe a method whereby each robot can determine the relative range, bear- ing and orientation of every other robot in the team, without the use of GPS, external landmarks, or instrumentation of the environment. We assume that robots are equipped with proprioceptive motion sensor (such as odometry or inertial measurement units) and some form of sensor that will al- low them to make occasional measurements of the relative pose and identity of nearby robots (such sensors can be con- structed using cameras or scanning laser range-nders). By employing a combination of maximum likelihood estimation and numerical optimization, we can subsequently infer the relative pose of every robot in the team at any given point in time. This paper describes the basic formalism, its practical im- plementation, and presents experimental results obtained using both real and simulated robots in both static and dy- namic environments.