Abstract: NASA's Mars exploration rovers' (MER) onboard mobility flight software was designed to provide robust and flexible operation. The MER vehicles can be commanded directly, or given autonomous control over multiple aspects of mobility: which motions to drive, measurement of actual motion, terrain interpretation, even the selection of targets of interest (although this mode remains largely underused). Vehicle motion can be commanded using multiple layers of control: motor control, direct drive operations (arc, turn in place), and goal-based driving (goto waypoint). Multiple layers of safety checks ensure vehicle performance: command limits (command timeout, time of day limit, software enable, activity constraints), reactive checks (e.g., motor current limit, vehicle tilt limit), and predictive checks (e.g., step, tilt, roughness hazards). From January 2004 through October 2005, Spirit accumulated over 5000 meters and Opportunity 6000 meters of odometry, often covering more than 100 meters in a single day. In this paper we describe the software that has driven these rovers more than a combined 11,000 meters over the Martian surface, including its design and implementation, and summarize current mobility performance results from Mars.

Abstract: Our research objective is Simultaneous Localization and Mapping (SLAM) in rubble environment. The map construction requires estimation of robot trajectory in 3D space. However, it is hard to estimate it by using odometry or gyro in rubble. In this paper, the authors proposed real-time SLAM based on 3D scan match. 3D camera is used for measurement of 3D shape and its texture in real-time. 3D map and robot trajectory are estimated by combining these 3D scan data. ICP algorithm is used for the matching method. The authors modified ICP algorithm as fast and robust one for real-time 3D map construction.

Abstract: An autonomous inspector mobile platform robot that is used to inspect a pipe or network of pipes. The robot includes a locomotion device that enables the device to autonomously progress through the pipe and accurately track its pose and odometry during movement. At the same time, image data is autonomously captured to detail the interior portions of the pipe. Images are taken at periodic intervals using a wide angle lens, and additional video images may be captured at locations of interest. Either onboard or offboard the device, each captured image is unwarped (if necessary) and combined with images of adjacent pipe sections to create a complete image of the interior features of the inspected pipe. Optional features include additional sensors and measurement devices, various communications systems to communicate with an end node or the surface, and/or image compression software.

Abstract: Visual Odometry is the process of estimating the movement of a (stereo) camera through its environment by matching point features between pairs of consecutive image frames. No prior knowledge of the scene nor the motion is necessary. In this work, we present a visual odometry approach using a specialized method of sparse bundle adjustment. We show experimental results that proof our approach to be a feasible method for estimating motion in unstructured outdoor environments.

Abstract: [1] By sol 440, the Spirit rover has traversed a distance of 3.76 km (actual distance traveled instead of odometry). Localization of the lander and the rover along the traverse has been successfully performed at the Gusev crater landing site. We localized the lander in the Gusev crater using two-way Doppler radio positioning and cartographic triangulations through landmarks visible in both orbital and ground images. Additional high-resolution orbital images were used to verify the determined lander position. Visual odometry and bundle adjustment technologies were applied to compensate for wheel slippage, azimuthal angle drift, and other navigation errors (which were as large as 10.5% in the Husband Hill area). We generated topographic products, including 72 ortho maps and three-dimensional (3-D) digital terrain models, 11 horizontal and vertical traverse profiles, and one 3-D crater model (up to sol 440). Also discussed in this paper are uses of the data for science operations planning, geological traverse surveys, surveys of wind-related features, and other science applications.

Abstract: Reliable navigation is a very important part of an autonomous mobile robot system. This means for instance that the robot should not lose track of its position, even if unexpected events like wheel slip and collisions occur. The standard approach to this problem is to construct a navigation system that is robust in itself. This paper proposes that detecting faults can also be made outside the normal navigation system, as an additional fault detector. Besides increasing the robustness, a means for detecting deviations is obtained, which can be important for the rest of the robot system, for instance the top level planner. The method uses two or more sources of robot position estimates, and compares them to detect unexpected deviation without getting deceived by drift or different characteristics in the position systems it gets information from. Both relative and absolute position sources can be used, meaning that existing positioning systems already implemented can be used in the detector. For detection purposes, an extended Kalman filter is used in conjunction with a CUSUM test. The detector is able to not only detect faults, but also give an estimate of when the fault occurred, which is useful for doing fault recovery. The detector is easy to implement, as it requires no modification of existing systems. Also the computational demands are very low. The approach is implemented and demonstrated on a mobile robot, using odometry and a scan matcher as sources of position information. It is shown that the system is able to detect wheel slip in real-time

Abstract: Most map building methods employed by mobile robots are based on the assumption that an estimate of robot poses can be obtained from odometry readings or from observing landmarks or other robots. In this paper we propose methods to build a global geometric map by integrating scans collected by laser range scanners without using any knowledge about the robots' poses. We consider scans that are collections of line segments. Our approach increases the flexibility in data collection, since robots do not need to see each other during mapping, and data can be collected by multiple robots or a single robot in one or multiple sessions. Experimental results show the effectiveness of our approach in different types of indoor environments

Abstract: We study the problem of registering local relative pose estimates to produce a global consistent trajectory of a moving robot. Traditionally, this problem has been studied with a flat world assumption wherein the robot motion has only three degrees of freedom. In this paper, we generalize this for the full six-degrees-of-freedom Euclidean motion. Given relative pose estimates and their covariances, our formulation uses the underlying Lie Algebra of the euclidean motion to compute the absolute poses. Ours is an iterative algorithm that minimizes the sum of Mahalanobis distances by linearizing around the current estimate at each iteration. Our algorithm is fast, does not depend on a good initialization, and can be applied to large sequences in complex outdoor terrains. It can also be applied to fuse uncertain pose information from different available sources including GPS, LADAR, wheel encoders and vision sensing to obtain more accurate odometry. Experimental results using both simulated and real data support our claim.

Abstract: This paper addresses an innovative method for the measurement and correction of systematic odometry errors caused by the kinematics imperfections in the differential drive mobile robots. An occasional systematic calibration of the mobile robot increases the odometric accuracy and reduces operational cost, as less frequent absolute positioning updates are required during the operation. Conventionally, the tests used for this purpose (e.g.. UMBmark test) are relatively difficult to perform and are very sensitive to non-systematic errors and requires a large number of tests with precise measurements of the final position and orientation of the robot to get better accuracy. This paper describes a novel method for calibration of differential drive mobile robots. The method is systematic, very simple to perform and insensitive to the random errors and hence provides near optimal results (with respect to the systematic errors) in a single test due to its inherent robustness. Simulation results are presented which shows the significant improvement in the odometric accuracy with less effort.

Abstract: All mobile bases suffer from localization errors. Previous approaches to accommodate for localization errors either use external sensors such as lasers or sonars, or use internal sensors like encoders. An encoder's information is integrated to derive the robot's position; this is called odometry. A combination of external and internal sensors will ultimately solve the localization error problem, but this paper focuses only on processing the odometry information. We solve the localization problem by forming a new odometry error model for the synchro-drive robot then use a novel procedure to accurately estimate the error parameters of the odometry error model. This new procedure drives the robot through a known path and then uses the shape of the resulting path to estimate the model parameters. Experimental results validate that the proposed method precisely estimates the error parameters and that the derived odometry error model of the synchro-drive robot is correct.

Abstract: In this paper we present a novel approach to estimating the position of objects tracked by a team of mobile robots and to use these objects for a better self localization. Modeling of moving objects is commonly done in a robo-centric coordinate frame because this information is sufficient for most low level robot control and it is independent of the quality of the current robot localization. For multiple robots to cooperate and share information, though, they need to agree on a global, allocentric frame of reference. When transforming the egocentric object model into a global one, it inherits the localization error of the robot in addition to the error associated with the egocentric model. We propose using the relation of objects detected in camera images to other objects in the same camera image as a basis for estimating the position of the object in a global coordinate system. The spatial relation of objects with respect to stationary objects (e.g., landmarks) offers several advantages: a) Errors in feature detection are correlated and not assumed independent. Furthermore, the error of relative positions of objects within a single camera frame is comparably small, b) The information is independent of robot localization and odometry. c) As a consequence of the above, it provides a highly efficient method for communicating information about a tracked object and communication can be asynchronous, d) As the modeled object is independent from robo-centric coordinates, its position can be used for self localization of the observing robot. We present experimental evidence that shows how two robots are able to infer the position of an object within a global frame of reference, even though they are not localized themselves and then use this object information for self- localization

Abstract: - This paper introduces a novel navigation system for walking persons The system is of particular benefit for emergency responders, who often have to enter and move around in large structures where GPS is unavailable We refer to our system as “Personal Odometry System” (POS) The POS measures the position of a walking person relative to a known starting position, such as the entrance to a building This is accomplished by instrumenting one boot of the subject with a 3-axis gyroscope and a 3-axis accelerometer (collectively called “inertial measurement unit” – IMU) This paper describes the POS hardware and explains the basics of our approach The paper also presents extensive experimental results, which illustrate the utility and practicality of our system I I NTRODUCTION This paper describes our Personal Odometry System (POS) for measuring and tracking the momentary location and trajectory of a walking person, even if GPS is not available We believe that such a system might be of particular value for emergency responders For example, fire fighters entering a burning building are at risk to be injured and unable to report their position With the POS reporting the user’s position to a central command post, each emergency responder’s location could be tracked in real-time Another application involves the “clearing” of a large building by emergency or security personnel Their challenge often is to keep track of rooms already cleared and areas that were not cleared, yet Our system’s ability to track each person’s location provides a useful solution for this problem Our proposed POS does not require GPS This is an important distinction, since GPS is not available indoors Furthermore, GPS is unreliable under dense foliage, in so-called “urban canyons,” and generally in any environment, in which a clear view of a good part of the sky is not available There are some approaches to personal position estimation without GPS Typically, these systems require external references, also called “fiducials,” such as preinstalled active beacons, receivers, or optical retroreflectors Common to all fiducial-based position estimation systems is that the fiducials must be installed in the work space at precisely surveyed locations before the system can be used This installation is time consuming and expensive, and in case of emergency response completely unfeasible Fiducial-based systems also require an active radiation source, such as infrared light [1], ultrasound [2], or magnetic fields [3], which may be undesirable in security-related applications Generally, fiducial-based systems perform well and are able to provide absolute position and orientation in real-time If the application permits the installation of fiducials ahead of time, then these systems have the significant advantage that errors don’t grow with time, as is the case in our POS Another way of implementing absolute position estimation is computer vision ([4] and [5]) Images are compared and matched against a pre-compiled database Computer vision has the advantage that the environment does not need to be modified, but the approach requires potentially very large databases Work is also being done on so-called Simultaneous Location and Mapping (SLAM) methods, which don’t require a precompiled database However, SLAM systems are not as reliable, may accrue errors over time and distance, and poor visibility and unfavorable light conditions can result in completely false position estimation [6][7] The scientific literature offers only very few approaches that do not require external references The simplest one of them is the pedometer, that is, a device that counts steps Pedometers must be calibrated for the stride length of the user and they produce large errors when the user moves in any other way than his or her normal walking pattern One commercially available personal navigation system based on this principle is the Dead Reckoning Module (DRM) by PointResearch [8] It uses accelerometers to identify steps, and linear displacement is computed assuming that the step size is constant Orientation is measured using a digital compass, which is combined with the traveled distance (step counts) to estimate 2-D position The performance of this system depends on the accuracy of determining the stride

Abstract: Performing reliable localisation and navigation within highly unstructured underwater coral reef environments is a difficult task at the best of times. Typical research and commercial underwater vehicles use expensive acoustic positioning and sonar systems which require significant external infrastructure to operate effectively. This paper is focused on the development of a robust vision-based motion estimation technique using low-cost sensors for performing real-time autonomous and untethered environmental monitoring tasks in the Great Barrier Reef without the use of acoustic positioning. The technique is experimentally shown to provide accurate odometry and terrain profile information suitable for input into the vehicle controller to perform a range of environmental monitoring tasks.

Abstract: In this paper, we present an approach of calculating visual odometry for outdoor robots equipped with a stereo rig. Instead of the typical feature matching or tracking, we use an improved stereo-tracking method that simultaneously decides the feature displacement in both cameras. Based on the matched features, a three-point algorithm for the resulting quadrifocal setting is carried out in a RANSAC framework to recover the unknown odometry. In addition, the change in rotation can be derived from infinity homography, and the remaining translational unknowns can be obtained even faster consequently . Both approaches are quite robust and deal well with challenging conditions such as wheel slippage.

Abstract: In this paper we derive theoretical results for the problem of on-line odometry self-calibration for a mobile robot. A first series of results regards the problem of understanding if a given system (consisting of a robot with several sensors) contains the necessary information to perform the on-line self calibration of the odometry. We consider several cases corresponding to different odometry systems and different types of robot sensors. Finally, we also consider the problem of maximizing the calibration accuracy and we formulate this problem as an optimal control problem. For the special case of a holonomic vehicle, we derive second order differential equations characterizing the solution, i.e. defining the best trajectory which maximizes the calibration accuracy.

Abstract: Most work on the simultaneous localization and mapping (SLAM) problem assumes the frequent availability of dense information about the environment such as that provided by a laser rangefinder. However, for implementing SLAM in consumer-oriented products such as toys or cleaning robots, it is infeasible to use expensive sensing. In this work we examine the SLAM problem for robots with very sparse sensing that provides too little data to extract features of the environment from a single scan. We modify SLAM to group several scans taken as the robot moves into multiscans, achieving higher data density in exchange for greater measurement uncertainty due to odometry error. We formulate a full system model for this approach, and then introduce simplifications that enable efficient implementation using a Rao-Blackwellized particle filter. Finally, we describe simple algorithms for feature extraction and data association of line and line segment features from multiscans, and then present experimental results using real data from several environments

Abstract: Actual solutions for the road vehicle navigation problem point to the combination of GPS, odometry and inertial sensors. To combine the information coming from these sensors, most of actual researchers rely on the implementation of variations of the Kalman filter (KF) and the extended Kalman filter (EKFF) for non-linear systems. Despite the fact that, in these filters, the definition of the proper vehicle model is of extreme importance, there is not a unique common filter suitable for all the usual situations in which a road vehicle is involved. The diversity of possible maneuvers and the need of realistic noise considerations adjusted to each driving situation encourage the application of IMM (interactive multi-model) techniques in the road navigation. Traditionally applied to the aerial sector, IMM based methods run different models at the same time, selecting that one which better represents the system behavior anytime. For road vehicles, the IMM-EKF solution presented in this paper allows the exploitation of highly dynamic models just when required, avoiding the impoverishment of the solution due to unrealistic noise considerations during straight or mild trajectories. Selected results presented in this paper confirm the improvements obtained by using the IMM-EKF developed, as compared with the single model solution.

Abstract: In the simultaneous localization and mapping (SLAM) problem, a mobile robot must build a map of its environment while simultaneously determining its location within that map. We propose a new algorithm, for visual SLAM (VSLAM), in which the robot's only sensory information is video imagery. Our approach combines stereo vision with a popular sequential Monte Carlo (SMC) algorithm, the Rao-Blackwellised particle filter, to simultaneously explore multiple hypotheses about the robot's six degree-of-freedom trajectory through space and maintain a distinct stochastic map for each of those candidate trajectories. We demonstrate the algorithm's effectiveness in mapping a large outdoor virtual reality environment in the presence of odometry error.

Abstract: Research at the University of Michigan's Mobile Robotics Lab aims at the development of an accurate Proprioceptive (i.e. without external references) Position Estimation (PPE) system for planetary rovers. Much like other PPE systems, ours uses an Inertial Measurement Unit (IMU) comprising three Fibre Optic Gyroscopes (FOGs) and a two-axes accelerometer, as well as odometry based on wheel encoders. Our PPE system combines data based on expert rules that implement our in-depth understanding of each sensor modality's behaviour under different driving and environmental conditions. Since our system also uses Fuzzy Logic operations in conjunction with the Expert Rules for finer gradation, we call it Fuzzy Logic Expert navigation (FLEXnav) PPE system. The paper presents detailed experimental results obtained with our FLEXnav system integrated with our planetary rover clone, Fluffy and operating in a Mars-like environment. In addition, we compare the results of our FLEXnav system with the results obtained from a conventional Kalman Filter (KF). The paper also introduces new methods for wheel slippage detection and correction, along with comprehensive experimental results.

Abstract: Heading information is critical for the control and/or navigation of mobile devices and robots. To get accurate heading information robustly, we propose a method which combines particle filtering with magnetic compasses. Although magnetic compasses can provide absolute heading angle, they have not been used for indoor applications since serious magnetic interferences are commonly founded in home/office environments. We overcome this difficulty by 1) suggesting statistical calibration of a magnetic compass, 2) deriving necessary conditions of the Earth's magnetic field area, and 3) designing an event-based particle filter based on likelihood calculated from conditional probability. Particle filter is an emerging key technology which can be applied to nonlinear/non-Gaussian model, beyond the limitations of Kalman filter. We take advantage of particle filter to optimally fuse the information from both magnetic compasses and odometry data. Experimental results on mobile robot navigation in indoor environments show reliability and robustness of the proposed method

Abstract: The authors present an innovative method for the efficient joint estimation of attitude and position in six degrees of freedom via sensors such as GPS, inertial measurement units, and odometry. Traditional methods for attitude estimation via Kalman filtering are beset by many conceptual problems relating to the representation of orientations in linear spaces, leading to difficulties in implementation and the interpretation of uncertainty estimates, among other issues. These problems are compounded when it is necessary to jointly estimate position and attitude. We demonstrate how Rao-Blackwellized particle filtering provides a framework for approaching this estimation problem that is both conceptually appealing and practical. Results are shown that demonstrate the filter's robustness to sensor outages and its ability to perform well even in situations with noisy sensors and high initial uncertainty in all state dimensions; these situations are precisely those in which traditional Kalman filtering approaches are most likely to experience problems

Abstract: The objective is to accurately determine mobile robots position and orientation by integrating information received from odometry and an inertial sensor. The position and orientation provided by odometry are subject to different types of errors. To improve the odometry, a fiber optic gyroscope is used to give the orientation information that is more reliable. The information from odometry and gyroscope are integrated using unscented Kalman filter (UKF). The position and orientation determined based on the UKF are compared with the results obtained from the commonly used extended Kalman filter (EKF).

Abstract: We present a parametric walk model for a four-legged robot. The walk model is improved using a genetic algorithm, but unlike previous approaches, the fitness is determined in a run that closely resembles the later application. We thus not only achieve high speeds, but also a high degree of flexibility. In addition to the walking model being flexible, we present a means of automatically calibrating the walking engine. This allows for highly precise robot control and greatly improved odometry accuracy. Lastly, we show how the motion model can be extended to integrate specialized motions to further increase locomotion speed without compromising flexibility.

Abstract: We present a parametric walk model for a four-legged robot. The walk model is improved using a genetic algorithm, but unlike previous approaches, the fitness is determined in a run that closely resembles the later application. We thus not only achieve high speeds, but also a high degree of flexibility. In addition to the walking model being flexible, we present a means of automatically calibrating the walking engine. This allows for highly precise robot control and greatly improved odometry accuracy. Lastly, we show how the motion model can be extended to integrate specialized motions to further increase locomotion speed without compromising flexibility.

Abstract: In this paper, we proposed a vision based global localization approach for intelligent vehicles. A single camera is used to determine vehicle's lateral and longitudinal offsets with respect to the road. Since the number of horizontal landmarks on the road is limited, an Extended Kalman Filter is used to fuse the results of odometry and vision, which also improves the system's reliability in case that landmarks disappear from camera's field of view. If locations of the landmarks are known a priori, the global pose of the vehicle can be estimated by the proposed methods. The algorithm is composed of two steps. landmarks detection using Randomized Hough Transform and data fusion with odometry. Experimental results with real data prove the high accuracy and low computation.

Abstract: Autonomous humanoid navigation in non-trivial environments requires high precision accuracy due to the difficulty in achieving stable bipedal locomotion In particular, an accurate localisation estimate is needed to plan footstep placement on a narrow staircase This paper reports the development of an accurate 6DOF particle filter based localisation system for a humanoid robot moving within a known 25 dimensional map A laser range sensor mounted within the robot's head makes 120 degree planar scans of the environment up to a distance of 4 meters Localisation accuracy is achieved by carefully characterising the robot's odometry model, introducing a novel motion model for particle prediction and decoupling the bounded and unbounded components of humanoid position uncertainty The novel motion predicts a more accurate particle distribution by modeling the motion of a humanoid robot and including uncertainty in sampling time as well as position accuracy In addition the reported system estimates the localisation uncertainty distribution and implements a model based gaze attraction behaviour to further reduce localisation error

Abstract: The odometry information used in localization can be quite erroneous when the robot follows the curved path or suffers from slippage. Thus the use of the low-cost gyroscope to compensate for an angular error is considered by many researchers. Conventional Kalman filtering methods that fuse the odometry with the gyroscope may produce infeasible solution because the robot parameters are estimated regardless of their physical constraints. In this paper, we propose a constrained Kalman filtering method that applies general constrained optimization technique to the estimation of the robot parameters. The state observability is improved by the additional state variables and the accuracy is also improved by the non-approximated Kalman filter design. Experimental results show the proposed method effectively compensates for the odometry error and yields feasible parameter estimation at the same time.

Abstract: A visual odometry system and method for a fixed or known calibration of an arbitrary number of cameras in monocular configuration is provided. Images collected from each of the cameras in this distributed aperture system have negligible or absolutely no overlap. The relative pose and configuration of the cameras with respect to each other are assumed to be known and provide a means for determining the three-dimensional poses of all the cameras constrained in any given single camera pose. The cameras may be arranged in different configurations for different applications and are made suitable for mounting on a vehicle or person undergoing general motion. A complete parallel architecture is provided in conjunction with the implementation of the visual odometry method, so that real-time processing can be achieved on a multi-CPU system.

Abstract: This paper explores how sensor and motion modeling can be improved to better Markov localization by exploiting deviations from expected sensor readings. Proprioception is achieved by monitoring target and actual motions of robot joints. This provides information about whether or not an action was executed as desired, yielding a quality measure of the current odometry. Odometry is usually extremely prone to errors for legged robots, especially in dynamic environments where collisions are often unavoidable, due to the many degrees of freedom of the robot and the numerous possibilities of motion hindrance. A quality measure helps differentiate the periods of unhindered motion from periods where robot motion was impaired for whatever reason. Negative evidence is collected when a robot fails to detect a landmark that it expects to see. Therefore the gaze direction of the camera has to be modeled accordingly. This enables the robot to localize where it could not when only using landmarks. In the general localization task, the probability distribution converges more quickly when negative information is taken into account.