Abstract: In this paper a sensor fusion scheme, called triangulation-based fusion (TBF) of sonar data, is presented. This algorithm delivers stable natural point landmarks, which appear in practically all indoor environments, i.e., vertical edges like door posts, table legs, and so forth. The landmark precision is in most cases within centimeters. The TBF algorithm is implemented as a voting scheme, which groups sonar measurements that are likely to have hit the same object in the environment. The algorithm has low complexity and is sufficiently fast for most mobile robot applications. As a case study, we apply the TBF algorithm to robot pose tracking. The pose tracker is implemented as a classic extended Kalman filter, which use odometry readings for the prediction step and TBF data for measurement updates. The TBF data is matched to pre-recorded reference maps of landmarks in order to measure the robot pose. In corridors, complementary TBF data measurements from the walls are used to improve the orientation and position estimate. Experiments demonstrate that the pose tracker is robust enough for handling kilometer distances in a large scale indoor environment containing a sufficiently dense landmark set.

Abstract: Mobile robots require the ability to build their own maps to operate in unknown environments. A fundamental problem is that odometry-based dead reckoning cannot be used to assign accurate global position information to a map because of drift errors caused by wheel slippage. The paper introduces a fast, online method of learning globally consistent maps, using only local metric information. The approach differs from previous 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.

Abstract: Autonomous robots and vehicles need accurate positioning and localization for their guidance, navigation and control. Often, two or more different sensors are used to obtain reliable data useful for control systems. The paper presents the data fusion system for mobile robot navigation. Odometry and sonar signals are fused using an Extended Kalman Filter (EKF) and Adaptive Fuzzy Logic System (AFLS). The signals used during navigation cannot be always considered as white noise signals. On the other hand, colored signals will cause the EKF to diverge. The AFLS was used to adapt the gain and therefore prevent Kalman filter divergence. The fused signal is more accurate than any of the original signals considered separately. The enhanced more accurate signal is used to guide and navigate the robot.

Abstract: We describe a new technique for landmark-based self-localization which is suitable for robots with poor odometry. This technique uses fuzzy logic to account for errors and imprecision in visual recognition, and for extreme uncertainty in the estimate of the robot's motion. It only requires an approximate model of the sensor system and a qualitative estimate of the robot's displacement, and it has a moderate computational cost. We show examples of use of our technique on a Sony AIBO legged robot in the RoboCup domain.

Abstract: We demonstrate that a simulated group of robots can co- operate to robustly transport resource between two areas in an unknown environment using an algorithm inspired by the trail following of ants and the waggle dance of honey bees. Rather than directly marking their environment, the robots announce their successful paths through a common localization space. It is found that the algorithm is robust to significant localization error, suggesting that the method will be viable for teams of real robots. relation between the representations maintained by two or more individuals. A prime example is the Global Position- ing System (GPS). Two systems equipped with GPS share a metric localization space in planetary coordinates. Simi- laxly two robots that start out with known positions in the same coordinate system and maintain a position estimate via odometry share a localization space. In both examples each robot has only an estimate of its position in the true space, but the true space is common to both. More abstract localization spaces can be considered, such as the location of a data byte in a hierarchical database or a URL on the Internet. In these cases too, there can be some uncertainty in position; for example if position is described by a fuzzy matching rule or an ambiguous data query. In this paper we demonstrate a cooperative transportation task in a group of simulated mobile robots that communi- cate by leaving landmarks in shared localization space. The method is shown to be robust with respect to significant lo- calization error; indicating that it should be suitable for use in real robots.

Abstract: We demonstrate that a simulated group of robots can co- operate to robustly transport resource between two areas in an unknown environment using an algorithm inspired by the trail following of ants and the waggle dance of honey bees. Rather than directly marking their environment, the robots announce their successful paths through a common localization space. It is found that the algorithm is robust to significant localization error, suggesting that the method will be viable for teams of real robots. relation between the representations maintained by two or more individuals. A prime example is the Global Position- ing System (GPS). Two systems equipped with GPS share a metric localization space in planetary coordinates. Simi- laxly two robots that start out with known positions in the same coordinate system and maintain a position estimate via odometry share a localization space. In both examples each robot has only an estimate of its position in the true space, but the true space is common to both. More abstract localization spaces can be considered, such as the location of a data byte in a hierarchical database or a URL on the Internet. In these cases too, there can be some uncertainty in position; for example if position is described by a fuzzy matching rule or an ambiguous data query. In this paper we demonstrate a cooperative transportation task in a group of simulated mobile robots that communi- cate by leaving landmarks in shared localization space. The method is shown to be robust with respect to significant lo- calization error; indicating that it should be suitable for use in real robots.

Abstract: Our research goal is to realize a robust navigation in indoor and outdoor environment for autonomous vehicle. An omnidirectional vehicle driven by four Mecanum wheels was chosen for our research platform. Mecanum wheel has 16 tilted rollers (45 degrees against the direction of wheel rotation) around the wheel, so the vehicle moves omnidirectionally by controlling these wheels independently. However, it has a disadvantage in odometry because of wheel slippage. Particularly, when the robot moves laterally, same wheels' rotations generate different traveling distance depending on friction of ground surface. To cope with the problem, we estimate robot's position by detecting optical flow of ground image using vision sensor (visual dead-reckoning). The estimation method is inaccurate comparing with odometry, but it is independent from friction of ground surface. Therefore, the estimated vehicle position can be improved by fusing odometry and visual dead-reckoning based on maximum likelihood technique. This paper describes an odometry method and a visual dead-reckoning method for omnidirectional vehicle, and fusion technique to improve the estimated position of the vehicle. Finally, experimental results support above technique.

Abstract: Landmark‐based navigation of autonomous mobile robots or vehicles has been widely adopted in industry. Such a navigation strategy relies on identification and subsequent recognition of distinctive environment features or objects that are either known a priori or extracted dynamically. This process has inherent difficulties in practice due to sensor noise and environment uncertainty. This paper is to propose a navigation algorithm that simultaneously locates the robots and updates landmarks in a manufacturing environment. A key issue being addressed is how to improve the localization accuracy for mobile robots in a continuous operation, in which the Kalman filter algorithm is adopted to integrate odometry data with scanner data to achieve the required robustness and accuracy. The Kohonen neural networks have been used to recognize landmarks using scanner data in order to initialize and recalibrate the robot position by means of triangulation when necessary.

Abstract: We present an approach to multi-robot exploration of large environments. Our method is designed to be robust in the face of arbitrarily large odometry errors or objects with poor reflectance characteristics. The algorithm achieves its robustness by using a team of cooperating agents. The critical aspect of our method is the use of a vision system that sweeps areas of free space and generates a graph-based description of the environment. This graph is used to guide the exploration process and can also be used for subsequent tasks such as place recognition or path planning. As a result of the guidance provided by the dual graph of the triangulated environment, our system can guarantee complete exploration without any overlaps.

Abstract: A state estimator design is presented for a Mars rover prototype. Odometry estimates are obtained by utilizing the fall kinematics of the vehicle including the nonlinear internal kinematics of the rover rocker-bogey mechanism as well as the contact kinematics between the wheels and the ground. Additional sensing using gyroscopes, accelerometers and visual sensors allows for robust rover motion state estimation. Simulation as well as experimental results are presented to illustrate the estimator operation.

Abstract: Addresses the problem of simultaneous localization and map building for a mobile robot moving in an unknown environment We show that the robustness of the techniques currently used can be notably enhanced when they are embedded in a sensor-based control scheme We are developing such an approach applied to a navigation task of an indoor mobile robot using a 2-D laser range finder The control objectives related to the sensor based control loop are defined thanks to the properties of the generalized Voronoi diagram A roadmap of the environment is incrementally built from the laser range information In this way the robot can explore an unknown scene without any reference trajectory or any prior knowledge about the environment The localization and mapping process is performed in a probabilistic sense by fusing laser and odometry data thanks to an extended Kalman filter

Abstract: This paper describes the means for generating rover localization information for NASA/JPL's FIDO rover. This is accomplished using a sensor fusion framework which combines wheel odometry with sun sensor and inertial navigation sensors to provide an integrated state estimate for the vehicle's position and orientation relative to a fixed reference frame. This paper describes two separate state estimation approaches built around the extended Kalman filter formulation and the Covariance Intersection formulation. Experimental results from runs in JPL's MarsYard are presented in order to compare the state estimates generated using each formulation.

Abstract: Height is a critical variable for helicopter hover control. In this paper we discuss, and present experimental results for, two different height sensing techniques: ultrasonic and stereo imaging, which have complementary characteristics. Feature-based stereo is used which provides a basis for visual odometry and attitude estimation in the future.

Abstract: This paper introduces a new automated guided vehicle (AGV) for guidewire-free industrial applications where rapid reconfiguration is required. The AGV, called OmniMate, has full omnidirectional motion capabilities, can correct odometry errors without external references, and offers a large 183 x 91 cm (72 x 36 in) loading deck. A patented, so-called compliant linkage avoids the excessive wheel slippage often found in other omnidirectional platforms. The paper describes the kinematic design and the control system of the platform and explains its unique odometry error correction method, called internal position error correction (IPEC). IPEC renders the OmniMate's odometry almost completely insensitive to even severe irregularities of the floor, such as bumps, cracks, or traversable objects. Dead-reckoning is enhanced further by the addition of a fibre-optics gyroscope. Because of its extraordinary dead-reckoning capabilities the OmniMate can travel over extended distances while following a pre-programmed pa...

Abstract: The contribution describes a robust but simple approach to 2D environment mapping making-use of a TOF-based laser ranging system. As the used mobile system has no absolute positioning system the task has been split into two parts. The first deals with preprocessing of odometry and range measurements towards determination of position and heading of the platform in the environment. The other step maintains (updates) the internal world model via search for correspondences between existing map and observed entities with subsequent application of renewal rules.

Abstract: Addresses path planning with uncertainty for a car-like robot subject to configuration uncertainty. The robot estimates its configuration with odometry and an absolute localization device based on environmental feature matching. The issue is to compute safe paths that guarantee that the goal will be reached in spite of the uncertainty. The solution proposed relies upon the automatic construction of a set of landmarks characterized by (1) a region of the configuration space, (2) the 'best' features for localization in this region, and (3) a perception uncertainty field that measures how well a feature is perceived at each configuration in the region. The landmarks are used within an efficient roadmap-based path planning algorithm that returns a safe motion plan that alternates motion along safe paths and localization, operations.

Abstract: An enhanced odometry technique based on the heading sensor called "clino-gyro" that fuses the data from a fiber optic gyro and a simple inclinometer is proposed. In the proposed scheme, inclinometer data are used to compensate for the gyro drift due to roll/pitch perturbation of the vehicle while moving on the rough terrain. Providing independent information about the rotation (yaw) of the vehicle, clino-gyro is used to correct differential odometry adversely affected by the wheel slippage. Position estimation using this technique can be improved significantly, however for the long term applications it still suffers from the drifts of the gyro and translational components of wheel skidding. Fusing this enhanced odometry with the data from environmental sensors (sonars, laser range finder) through Kalman filter-type procedure a reliable positioning can be obtained. This technique has been implemented on-board of an experimental skid-steered vehicle. Obtained precision is sufficient for navigation in underground mining drifts. For open-pit mining applications further improvements can be obtained by fusing proposed localization algorithm with GPS data.

Abstract: An integrated multi-sensor vehicle navigation system is presented which uses a low cost rate gyro and differential odometry to supplement GNSS availability under signal masking. The purpose of the system is to provide various applications with 10 m accuracy nearly 100% of the time. The equipment used is discussed in detail, and the method of filtering the data is explained. Results from two tests with simulated GPS signal masking for 100 and 200 s intervals are presented. Tests demonstrate that the use of differential odometry augmentation does not reach the 10 m specifications, but with the use of a gyro and odometry they are nearly met. After further tuning of the filters and data processing techniques, these specifications will likely be met.

Abstract: This paper describes the theoretical development and evaluation of the multisensor navigation system for high speed road vehicle. The paper focuses on the design of the nonlinear process model that is able to cope with vehicle slip using multisensor data from the inertial sensors, odometry, and D-GPS. The algorithm was evaluated using a vehicle dynamics simulator built to allow the simulation of a wide variety of driving scenarios. The simulation results show that the scheme is able to significantly reduce the errors in vehicle position and orientation estimates. They also show that the scheme allows slip angle estimation and accelerometer bias estimation.

Abstract: This paper proposes a conceptual design for a distributed system of very simple robots capable of performing a useful realworld mission such as mapping the interior of a building overnight with a swarm of possibly hundreds of cockroach-sized robots. Presentation of this system concept follows an initial discussion of strategies for developing distributed robotic systems. Success is dependent on making good decisions in selecting appropriate applications, in system design, and in executing the system development process. Each robot includes basic mobility, crude odometry, contact or near-contact object/obstacle detection sensors, an omnidirectional beacon (probably IR), and a beacon detection sensor that can simultaneously detect multiple beacons on other robots and measure the bearing of each to less than one degree. Beacon triangulation (combined with knowledge of some baseline distance) allows the determination of the position of any robot (and any object next to it) relative to the others. Occlusion of a robot’s beacon indicates the presence of an intervening object, while lack of occlusion identifies a “ray” of free space. Clever deployment of large numbers of robots will permit mapping of walls and objects, and can also support the detection and localization of intruders moving within the space. The object sensor can be very short range and, therefore, hopefully very simple and cheap, perhaps implemented as an array of whiskers. The beacon sensor is more complex, but it can be completely tuned to the detection of the cooperating beacon. Thus, the robots need tackle no perception tasks whatsoever. The system development begins with the implementation of an initial baseline system, in which each robot is controlled by a central coordinating element via a high bandwidth communications link. This initial effort will develop and validate behaviors and algorithms, assess sensitivity to sensor error, determine communications and processing requirements, and generally expedite the system’s evolution into a fully distributed system.

Abstract: We present a method of route selection for a vision guided mobile robot with art odometry system, given an uncertain map. The robot first generates candidates of the routes, which are expanded to possible loci in consideration of the map uncertainty. It then makes an observation and movement plan along each locus and computes the minimum traveling time. Uncertainties of movement and visual sensing are considered in this step. Finally, for each candidate route, the robot computes its expected traveling time from probabilities of loci It selects the route with the least expected traveling time as the optimal route. The advantage of the planning method is that the observation and movement planning are incorporated in path planning to obtain the optimal plan. The three sources of uncertainty are considered in a unified manner in our method. The method has been implemented and tested to validate our claim.

Abstract: This paper describes the theoretical development and evaluation of a multisensor navigation system for high speed road vehicles. The particular contributions of the theoretical work are in the designing the nonlinear process model that is able to cope with vehicle slip using multisensor data from inertial sensors, odometry and the D-GPS. The algorithm was evaluated using a vehicle dynamics simulator built to allow the simulation of a wide variety of driving scenarios. The simulation results indicate that the scheme is able to significantly reduce the errors in vehicle position and orientation estimates. Besides, the results also show that the scheme allows slip angle estimation (which is of importance in itself), and accelerometer bias estimation.

Abstract: We discuss a prototype problem involving terrain exploration and learning by formations of autonomous vehicles. We investigate an algorithm for coordinating multiple robots whose task is to nd the shortest path between a xed pair of start and target locations, without access to a global map containing those locations. Odometry information alone is not sucient for minimizing path length if the terrain is uneven or if it includes obstacles. We generalize existing results on a simple control law, also known as \local pursuit", which is appropriate in the context of formations and which requires limited interaction between vehicles. Our algorithm is iterative and converges to a locally optimal path. We include simulations and experiments illustrating the performance of the proposed strategy.

Abstract: Regarding assistance to disabled people for object manipulation and carrying, the paper focuses on the localisation for mobile robot autonomy. In order to respect strong low-cost constraints, the perception system of the mobile robot uses sensors of low metrological quality, ultrasonic ring and odometry. That poses new problems for localisation, in particular. Among different localisation techniques, we present only off-line localisation. With poor perception means, it is necessary to introduce a priori knowledge on sensors and environment models. To solve the localisation problem, the ultrasonic image is segmented applying the Hough transform, well-adapted to ultrasonic sensor characteristics. The segments are then matched with the room, modelled and assumed to be rectangular. Several positions are found. A first sort, based on a cost function, reduces the possibilities. The remaining ambiguities are removed by a neural network which plays the part of a classifier detecting the door in the environment. Improvements of the method are proposed to take into account obstacles and non-rectangular room. Experimental results show that the localisation operates even with one obstacle.

Abstract: This work investigates the propagation of errors from the camera motion to the epipolar constraint. A relation between the perturbation of the motion parameters and the error in the epipolar constraint is derived. Based on this relation, the sensitivity of the motion parameters to the epipolar constraint is characterised, and a constraint on the allowed perturbation in a motion parameter in response to a threshold for the produced error in the epipolar constraint is determined. The presented error propagation model is useful to vision systems such as a mobile robot where the camera motion is provided by an odometry sensor. Experimental results on real images are presented.

Abstract: This paper discusses the possible use of strapdown inertial navigation for real-time ground penetrating radar (GPR) antenna position and orientation estimation along arbitrary three-dimensional acquisition lines. Strapdown inertial navigation theory has been studied extensively in the literature for aircraft, missile and space navigation. Here, we give an overview of the theory as it applies to the antenna position and orientation problem. This includes the definition of the relevant coordinate frames and attitude parameters, a discussion of the measured acceleration and angular velocity, and a description of the four primary computational tasks pertinent to strapdown inertial navigation. These are the initial alignment of the system, the integration of angular velocity into attitude (attitude updating), the acceleration transformation and integration into velocity (velocity updating) and the integration of velocity into position (position updating). The key elements of using a low-grade versus a high-grade inertial measurement unit (IMU) are pointed out. The actual performance of a commercially available low-grade IMU is evaluated based on a series of navigation experiments. The experimental results show that the tested IMU is far from being accurate enough for completely self-contained antenna positioning and that the precise calibration for scale factors, biases and axis misalignments is vital. The observed orientation accuracy (error of less than 1 degree after 60 seconds) suggests the integration of the tested IMU with odometry, extending the applicability of the latter to environments with topography or where changing of the profile direction due to obstacles is necessary. Another possible use of low-grade IMUs might be for more sophisticated 'rubber sheeting' techniques.

Abstract: This paper proposes a dead reckoning of a modular omnidirectional vehicle. Wheel units assembled in the vehicle are modularized on their mechanisms and controllers, so that we can easily tailor the vehicle to individual transfer applicatoins. Each of the wheel modules estimates the vehicle position based on the information of the odometry and gyro. The wheel modules exchange their local estimates. The local estimates are fused in a decentralized manner, and then the wheel modules can precisely identify the vehicle position under the conditions of wheel slips and changes of wheel radius. The dead reckoning algorithm is formulated based on the extended Kalman filter, and some fusion rules are incorporated into the algorithm to improve the accuracy of the dead reckoning. Simulation and experimental results show that our dead reckoning can provide better positioning accuracy than the conventional dead reckoning.