Abstract: Work in simultaneous localisation and map-building ("SLAM") for mobile robots has focused on the simplified case in which a robot is considered to move in two dimensions on a ground plane. While this is often a good approximation, a large number of real-world applications require robots to move around terrain which has significant slopes and undulations, and it is desirable that these robots too should be able to estimate their locations by building maps of natural features. We describe a real-time EKF-based-SLAM system permitting unconstrained 3D localisation, and in particular develop models for the motion of a wheeled robot in the presence of unknown slope variations. In a fully automatic implementation, our robot observes visual point features using fixating stereo vision and builds a sparse map on-the-fly. Combining this visual measurement with information from odometry and a roll/pitch accelerometer sensor, the robot performs accurate, repeatable localisation while traversing an undulating course.

Abstract: Fiber optic gyros with very low drift rates have become available and affordable. Because of their low drift rate attention is warranted to sources of errors that were previously considered as of secondary importance. In the KVH E-Core RD2100 gyros, we found that the temperature dependency and the non-linearity of the scale-factor caused significant errors. A precision calibration procedure, described in the paper, reduces the resulting errors by one order of magnitude. In addition to the calibration procedure, we integrated the gyro with the odometry system of a four-wheel drive, skid-steer Pioneer AT mobile robot by means of an indirect feedback Kalman filter that fuses the sensor data from both sensor modalities. Based on our experimental results the paper compares the relative effectiveness of both enhancements (precision gyro calibration and Kalman filter).

Abstract: In robotics, homing can be defined as that behavior which enables a robot to return to its initial (home) position, after traveling a certain distance along an arbitrary path. Odometry has traditionally been used for the implementation of such a behavior, but it has been shown to be an unreliable source of information. In this work, a novel method for visual homing is proposed, based on a panoramic camera. As the robot departs from its initial position, it tracks characteristic features of the environment (corners). As soon as homing is activated, the robot selects intermediate target positions on the original path. These intermediate positions (IPs) are then visited sequentially, until the home position is reached. For the robot to move between two consecutive IPs, it is only required to establish correspondence among at least three corners. This correspondence is obtained through a feature tracking mechanism. The proposed homing scheme is based on the extraction of very low-level sensory information, namely the bearing angles of corners, and has been implemented on a robotic platform. Experimental results show that the proposed scheme achieves homing with a remarkable accuracy, which is not affected by the distance traveled by the robot.

Abstract: Knowledge of position in the context of its surrounding is necessary for robots to build maps and develop path plans. Limitations in odometry and the lack of a priori knowledge reduce the effectiveness of a single robot to retain a sense of position for any extended duration. The problem is only compounded when the scale of the robot is reduced. However, by employing multiple robots we can exploit their distributed nature to provide an external context in which to evaluate sensor readings for mapping and localization. We have designed a team of centimeter-sized robots that coordinate sensing and action to establish and maintain position as they move throughout space. By utilizing low-cost ultrasonic sensors, the team is able to measure the range between each robot pair. We pose these measurements in terms of a position likelihood and combine them to find a global solution that best maximizes the position likelihood of each robot. We also address a unique multipath interference mode that arises as a direct result of the reduced scale of the robot team. We present our experiences with localization and control of a small robot team.

Abstract: This paper describes the development of a two-tier state estimation approach for NASA/JPL's FIDO Rover that utilizes wheel odometry, inertial measurement sensors, and a sun sensor to generate accurate estimates of the rover's position and attitude throughout a rover traverse. The state estimation approach makes use of a linear Kalman filter to estimate the rate sensor bias terms associated with the inertial measurement sensors and then uses these estimated rate sensor bias terms to compute the attitude of the rover during a traverse. The estimated attitude terms are then combined with the wheel odometry to determine the rover's position and attitude through an extended Kalman filter approach. Finally, the absolute heading of the vehicle is determined via a sun sensor which is then utilized to initialize the rover's heading prior to the next planning cycle for the rover's operations. This paper describes the formulation, implementation, and results associated with the two-tier state estimation approach for the FIDO rover.

Abstract: Vehicle odometry is a nonlinear dynamical system in echelon form. Accordingly, a general solution can be written by solving the nonlinear equations in the correct order. Another implication of this structure is that a completely general solution to the linearized (perturbative) dynamics exists. The associated vector convolution integral is the general relationship between the output error and both the input error and reference trajectory. Solutions for errors in individual coordinates are in the form of line integrals in state space. Response to initial conditions and translational scale errors, among others, is path independent and vanishes on all closed trajectories. Response to other errors is path dependent and can be reduced to expressions in error moments of the reference trajectory. These path dependent errors vanish on closed symmetric paths, among others. These theoretical results and the underlying error expressions have many uses in design, calibration, and evaluation of odometry systems.

Abstract: An integrated multi-sensor vehicle navigation system is presented that uses a low-cost rate gyro and differential odometry to supplement GPS under signal masking conditions such as tree foliage and urban canyons. Signal masking is often accompanied by extreme multi-path in urban centres with tall buildings, and is also found in wooded areas, enclosed car parks, tunnels, etc. The purpose of the system tested is to provide an accuracy of better than 20 metres almost 100 % of the time throughout these interruptions, which are assumed to last up to a few minutes. The equipment used is discussed in detail, as is the method used for filtering measurements. Results are presented from tests carried out in an urban core with relatively long periods of signal loss - up to several minutes over a 6-km test circuit. Tests in urban canyons demonstrate that it is difficult to reach the above specifications with aiding from differential odometry alone due to the high precision of the wheel-scale factor required. However, with the use of a rate gyro and odometry, RMS errors are below 20 metres while availability is nearly 100 %. Some of the large deviations could probably be better controlled if GPS multi-path errors were detected before they are allowed to corrupt the filtered solution.

Abstract: We address the problem of autonomous navigation and localization in indoor environments, referring in particular to the specific scenario of service mobile robotics applications. The localization system uses active beacons (i.e. active transponders distributed throughout the building) as reference points; the estimate of the position of the robot and its uncertainty, both retrieved by correcting the estimate provided by odometry through an extended Kalman filter, are fed to the navigation system in order to help the robot to plan and execute target-oriented navigation tasks while showing a reactive behavior to handle the unpredictability of the environment.

Abstract: Calibration of a differential scale factor associated with left and right wheels of a vehicle having a terrestrial navigation system. The terrestrial navigation system includes a GPS receiver integrated with a dead reckoning system. The dead reckoning system has wheel sensors coupled to the left and right wheels. The calibration of the differential scale factor includes: determining a heading change with the dead reckoning system, using the determined heading change to determine an open loop heading, determining an error in the differential scale factor based on the open loop heading and an alternate heading, and using the error in the differential scale factor to adjust an initial value of the differential scale factor.

Abstract: Sensor-based localization is one of the fundamental problems in mobile robots. We present a technique for online robot localization in an a priori known indoor environment. Our approach uses the local Voronoi diagram, generated from a laser range scan, to match it against the global Voronoi diagram of the robot's workspace. The result from this process is used to estimate the robot's position in the map or to correct the robot's odometry. Experiments with real data are presented to validate this algorithm.

Abstract: The disclosed invention is an improvement on the traditional Measuring Wheel. When the odometry information is combined with two direction sensors and an on-board computer, the instrument is able to perform useful measurements to allow the calculation of an area or the description of a non-linear contour, as well as the traditional distance measurements.

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 leader/follower behaviors, with one or more followers tracking the pat+6|+ken 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 for a small urban robot. This 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.© (2001) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

Abstract: This thesis presents a recent research on the problem of environmental modeling for both localization and map building for wheel-based, differential driven, fully autonomous and self-contained mobile robots. The robots behave in an indoor office environment. They have a multi-sensor setup where the encoders are used for odometry and two exteroperceptive sensors, a 360° laser scanner and a monocular vision system, are employed to perceive the surrounding. The whole approach is feature based meaning that instead of directly using the raw data from the sensor features are firstly extracted. This allows the filtering of noise from the sensors and permits taking account of the dynamics in the environment. Furthermore, a properly chosen feature extraction has the characteristic of better isolating informative patterns. When describing these features care has to be taken that the uncertainty from the measurements is taken into account. The representation of the environment is crucial for mobile robot navigation. The model defines which perception capabilities are required and also which navigation technique is allowed to be used. The presented environmental model is both metric and topological. By coherently combining the two paradigms the advantages of both methods are added in order to face the drawbacks of a single approach. The capabilities of the hybrid approach are exploited to model an indoor office environment where metric information is used locally in structures (rooms, offices), which are naturally defined by the environment itself while the topology of the whole environment is resumed separately thus avoiding the need of global metric consistency. The hybrid model permits the use of two different and complementary approaches for localization, map building and planning. This combination permits the grouping of all the characteristics which enables the following goals to be met: Precision, robustness and practicability. Metric approaches are, per definition, precise. The use of an Extended Kalman Filter (EKF) permits to have a precision which is just bounded by the quality of the sensor data. Topological approaches can easily handle large environments because they do not heavily rely on dead reckoning. Global consistency can, therefore, be maintained for large environments. Consistent mapping, which handle large environments, is achieved by choosing a topological localization approach, based on a Partially Observable Markov Decision Process (POMDP), which is extended to simultaneous localization and map building. The theory can be mathematically proven by making some assumptions. However, as stated during the whole work, at the end the robot itself has to show how good the theory is when used in the real world. For this extensive experimentation for a total of more than 9 km is performed with fully autonomous self-contained robots. These experiments are then carefully analyzed. With the metric approach precision with error bounds of about 1 cm and less than 1 degree is further confirmed by ground truth measurements with a mean error of less than 1 cm. The topological approach is successfully tested by simultaneous localization and map building where the automatically created maps turned out to work better than the a priori maps. Relocation and closing the loop are also successfully tested.

Abstract: A security system called SCMT, to be installed on trains circulating on Italian railways is being developed. One of the components of SCMT is a module for estimating train speed and positions between two subsequent viapoints equipped with balises which communicate to the train the distance to next target(s) and velocity requirement(s) at target(s). The module uses two wheels equipped with incremental encoder-type sensors. We describe an algorithm for position and velocity estimation capable of compensating for poor wheel-rail adhesion conditions due to rain, fog, ice, leaves, and so on, where conventional odometry algorithms fail. The system was designed and trained using a wide set of experimental data, conducted using different types of vehicles and conditions (in particular, degraded adhesion conditions were investigated). In each experimental test the velocities of four wheels and the real train were measured. The main goal of the system proposed in the paper is to evaluate the train speed and position on the basis of two wheel velocities information also in the case of wheel slipping or skidding. The system can estimate train speed and position also in case of failure of one of the two velocity sensors. The velocities of the two wheels must be periodically re-aligned to the real train speed, during a phase in which wheels are not slipping, in order to compensate for variations of wheel diameters.

Abstract: The odometry error of a mobile robot contains both systematic and non-systematic components. This paper presents an error modeling of an odometry system for a synchronous drive system and a possible strategy in order to evaluate this error. The odometry error is modeled by introducing four parameters characterizing its systematic and non-systematic components (translational and rotational). We introduce five experimentally measurable quantities for a given robot motion, which we call observables. On the basis of our odometry error model we analytically compute the average values of the observables, which depend on the previous four parameters and on the considered robot motion. We suggest a possible strategy in order to simultaneously estimate the four parameters by estimating the observables. We show that our strategy only requires to measure the change in the orientation and position between the initial and the final configuration of the robot related to suitable robot motions. The strategy was applied to the platform Nomad150.

Abstract: The purpose of the study is to realize autonomous navigation of a mobile robot in an indoor environment by teaching and playback method, without making any environmental map in advance. The strategy of navigation in the study is that the robot runs based on its odometry information and corrects its position using landmark information obtained from a stereo vision sensor. We use a trinocular vision system to solve easily the stereo matching problem and choose vertical lines in the environment as landmarks because of their easiness to detect and sufficient existence in the environment. In the paper, the method of teaching and playback is described and some experimental results are also shown.

Abstract: A low cost navigation system is developed which fuses inertial sensor information provided by gyroscopes and odometry information Both Kalman filters and a rule set based fusion strategy are used to correct the odometry errors in orientation for mobile robots The fusion system even improves orientation estimation using a gyroscope with an extremely high drift rate This navigation system is implemented on the autonomous mobile robot B21 The performance of this fusion method is tested on different surfaces and orientation errors do not increase as the robot travels along Experimental results demonstrate that even in harsh environments with obstacles the performance of the designed navigation system reduces odometry tracking errors The effects of nonsystematic odometry errors caused by unpredictable large bumps or objects encountered on the floor are reduced

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: Demonstrates fuzzy control of heading direction for a mobile robot. The robot is engaged in a docking maneuver guided by an active perceptual behaviour. In order to dock, the robot must control its heading direction to move directly towards a target object. Previously, we have developed a technique in which the robot fixates on the desired target, and corrects its heading direction based on information derived from optical flow data from a log-polar camera. However, the optical flow data derived is noisy. Thus, when robot direction control is based directly on the optical flow data the resulting path is erratic. A smooth path is more direct and so faster, leads to less strain on motors, and simplifies fixation. Further, constant change in direction can result in wheel-slippage, leading to errors in odometry. The results show a significant smoothing of the robot path in simulation.

Abstract: This paper describes the first results of the investigation efforts performed in the development of the high-accuracy multisensor vehicle state estimation scheme. The use of UKF (Unscented Kalman Filter) in the state estimation scheme and vehicle model development framework is proposed. The first nonlinear vehicle model developed in this framework is also described. The model is able to cope with vehicle slip using multisensor data from inertial sensors, odometry, and the D-GPS. The simulation results indicated that the scheme is able to significantly reduce the errors in vehicle state estimates and is also able to perform real time internal sensors calibration.

Abstract: To improve safety and efficiency in the management of modern railways, a novel security system named SCMT, to be installed on trains circulating in Italian railways, is currently being developed. One of the components of SCMT is a module for estimating train speed and positions between balises, which communicate to the train the distance to next target and velocity requirement at target. Correct estimate of distance to target and actual velocity is crucial to evaluate residual braking resources, in terms of available deceleration, to reach the target at the required speed. Odometry techniques based on sensors located on one or more axles of the train may help in solving the problem of dead reckoning between two subsequent exact position assessments. Dead reckoning by means of odometry may fail if degraded adhesion in the wheel-rail interaction occurs, due to rain, fog, ice, leaves, and so on, and the train is accelerating or braking, i.e. when pure rolling conditions do not hold anymore, and macroscopic sliding occurs on one or more of the axles equipped with odometry sensors. The module uses two axles equipped with incremental encoder-type sensors. In this paper we describe an algorithm for distance to target and velocity estimation, capable of compensating for poor wheel-rail adhesion conditions where conventional odometry algorithms may fail. The system was designed and trained using a wide set of experimental data, obtained from tests carried out using different types of vehicles and conditions (in particular, degraded adhesion conditions were investigated). In each experimental test the velocities of four axles and the real train speed were measured. The system can be subdivided into three main blocks. The first one acquires axle velocities from the sensors and evaluates axle accelerations. The system runs with a sampling period of about 100 ms, since smaller sampling periods have shown not to improve significantly the system performance. Axle accelerations are estimated by finite differences. A further filtering block is used to eliminate noise from acceleration signals. The second main block is responsible for the calculation of two state variables accounting for example for the adhesion condition of each of the two axles. This block is also responsible for the choice of speed estimation procedure. Seven different procedures are provided, to take into account the adhesion conditions and if the train is accelerating or braking. The third main block receives as input state variable values and the values of axle velocities and accelerations. Using crisp reasoning rules based on experience concerning wheel slide protection systems, this last block generates the estimate of train speed and travelled distance. Experimental results on the validation of the method will be presented along with a performance comparison with alternative black boxes method based on fuzzy inference systems and neural networks of the multilayer perceptron type.

Abstract: Most mobile robots use a combination of absolute and relative sensing techniques for position estimation Relative positioning techniques are generally known as dead-reckoning Many systems use odometry as their only dead-reckoning means However, in recent years fiber optic gyroscopes have become more affordable and are being used on many platforms to supplement odometry, especially in indoor applications Still, if the terrain is not level (ie, rugged or rolling terrain), the tilt of the vehicle introduces errors into the conversion of gyro readings to vehicle heading In order to overcome this problem vehicle tilt must be measured and factored into the heading computation This technical report introduces a new fuzzy logic expert rule-based method for fusing data from multiple low- to medium-cost gyroscopes and accelerometers in order to estimate accurately the attitude (ie, heading and tilt) of a mobile robot The attitude information is then further fused with wheel encoder data to estimate the three-dimensional position of the mobile robot Experimental results of mobile robot runs over rugged terrain are presented, showing the effectiveness of our fuzzy logic rule-based sensor fusion method

Abstract: The problem of mobile robot localization by using sensor information appeals to different communities since the need for accurate position has become crucial for many robot subtasks. The Kalman filter (KF) has been acknowledged as an appropriate tool for a suitable dynamic combination of the different measurements using the state and measurement models. However, when there are discrete uncertainties about the models, without additional restrictions, the performance of KF degrades drastically as the predicted estimate tends to be updated by a wrong measurement. This paper focuses on a suboptimal multiple model filter to deal with the associated measurement/model. Experimental and simulated results are achieved, which prove the feasibility of the proposal. The experimental setup consists of a structured environment constituted of elementary features such as walls and corners, while a possibly unmodeled obstacle may be encountered. The robot is equipped with odometry and a set of ultrasonic sensors.

Abstract: A differentially steered three-wheeled vehicle has proven to be an effective platform for outdoor navigation. Many applications for this vehicle configuration, including planetary exploration and landmine/UXO location, require accurate localization. In spite of known problems, odometry, also called dead reckoning, remains one of the least expensive and most popular methods for localization. This paper presents the results of an investigation into the benefits of instrumenting the rear caster wheel to supplement the drive wheel encoders in odometry. A linear observer is used to fuse the data between the drive wheel encoders and the caster data. This method can also be extended using the standard form of the Kalman filter to allow for noise. Improvements in position estimation in the face of common problems such as slip and dimensional errors are quantified.