Kinematics Based Visual Localization for Skid-Steering Robots: Algorithm and Theory

To build commercial robots, skid-steering mechanical design is of increased popularity due to its manufacturing simplicity and unique mechanism. However, these also cause significant challenges on software and algorithm design, especially for pose estimation (i.e., determining the robot's rotation and position), which is the prerequisite of autonomous navigation. While the general localization algorithms have been extensively studied in research communities, there are still fundamental problems that need to be resolved for localizing skid-steering robots that change their orientation with a skid. To tackle this problem, we propose a probabilistic sliding-window estimator dedicated to skid-steering robots, using measurements from a monocular camera, the wheel encoders, and optionally an inertial measurement unit (IMU). Specifically, we explicitly model the kinematics of skid-steering robots by both track instantaneous centers of rotation (ICRs) and correction factors, which are capable of compensating for the complexity of track-to-terrain interaction, the imperfectness of mechanical design, terrain conditions and smoothness, and so on. To prevent performance reduction in robots' lifelong missions, the time- and location- varying kinematic parameters are estimated online along with pose estimation states in a tightly-coupled manner. More importantly, we conduct in-depth observability analysis for different sensors and design configurations in this paper, which provides us with theoretical tools in making the correct choice when building real commercial robots. In our experiments, we validate the proposed method by both simulation tests and real-world experiments, which demonstrate that our method outperforms competing methods by wide margins.Comment: 18 pages in tota

Review and classification of vision-based localisation techniques in unknown environments

International audienceThis study presents a review of the state-of-the-art and a novel classification of current vision-based localisation techniques in unknown environments. Indeed, because of progresses made in computer vision, it is now possible to consider vision-based systems as promising navigation means that can complement traditional navigation sensors like global navigation satellite systems (GNSSs) and inertial navigation systems. This study aims to review techniques employing a camera as a localisation sensor, provide a classification of techniques and introduce schemes that exploit the use of video information within a multi-sensor system. In fact, a general model is needed to better compare existing techniques in order to decide which approach is appropriate and which are the innovation axes. In addition, existing classifications only consider techniques based on vision as a standalone tool and do not consider video as a sensor among others. The focus is addressed to scenarios where no a priori knowledge of the environment is provided. In fact, these scenarios are the most challenging since the system has to cope with objects as they appear in the scene without any prior information about their expected position

Invariant EKF Design for Scan Matching-aided Localization

Localization in indoor environments is a technique which estimates the robot's pose by fusing data from onboard motion sensors with readings of the environment, in our case obtained by scan matching point clouds captured by a low-cost Kinect depth camera. We develop both an Invariant Extended Kalman Filter (IEKF)-based and a Multiplicative Extended Kalman Filter (MEKF)-based solution to this problem. The two designs are successfully validated in experiments and demonstrate the advantage of the IEKF design

Vision-Aided Pedestrian Navigation for Challenging GNSS Environments

There is a strong need for an accurate pedestrian navigation system, functional also in GNSS challenging environments, namely urban areas and indoors, for improved safety and to enhance everyday life. Pedestrian navigation is mainly needed in these environments that are challenging for GNSS but also for other RF positioning systems and some non-RF systems such as the magnetometry used for heading due to the presence of ferrous material. Indoor and urban navigation has been an active research area for years. There is no individual system at this time that can address all needs set for pedestrian navigation in these environments, but a fused solution of different sensors can provide better accuracy, availability and continuity. Self-contained sensors, namely digital compasses for measuring heading, gyroscopes for heading changes and accelerometers for the user speed, constitute a good option for pedestrian navigation. However, their performance suffers from noise and biases that result in large position errors increasing with time. Such errors can however be mitigated using information about the user motion obtained from consecutive images taken by a camera carried by the user, provided that its position and orientation with respect to the user’s body are known. The motion of the features in the images may then be transformed into information about the user’s motion. Due to its distinctive characteristics, this vision-aiding complements other positioning technologies in order to provide better pedestrian navigation accuracy and reliability. This thesis discusses the concepts of a visual gyroscope that provides the relative user heading and a visual odometer that provides the translation of the user between the consecutive images. Both methods use a monocular camera carried by the user. The visual gyroscope monitors the motion of virtual features, called vanishing points, arising from parallel straight lines in the scene, and from the change of their location that resolves heading, roll and pitch. The method is applicable to the human environments as the straight lines in the structures enable the vanishing point perception. For the visual odometer, the ambiguous scale arising when using the homography between consecutive images to observe the translation is solved using two different methods. First, the scale is computed using a special configuration intended for indoors. Secondly, the scale is resolved using differenced GNSS carrier phase measurements of the camera in a method aimed at urban environments, where GNSS can’t perform alone due to tall buildings blocking the required line-of-sight to four satellites. However, the use of visual perception provides position information by exploiting a minimum of two satellites and therefore the availability of navigation solution is substantially increased. Both methods are sufficiently tolerant for the challenges of visual perception in indoor and urban environments, namely low lighting and dynamic objects hindering the view. The heading and translation are further integrated with other positioning systems and a navigation solution is obtained. The performance of the proposed vision-aided navigation was tested in various environments, indoors and urban canyon environments to demonstrate its effectiveness. These experiments, although of limited durations, show that visual processing efficiently complements other positioning technologies in order to provide better pedestrian navigation accuracy and reliability

Information Aided Navigation: A Review

The performance of inertial navigation systems is largely dependent on the stable flow of external measurements and information to guarantee continuous filter updates and bind the inertial solution drift. Platforms in different operational environments may be prevented at some point from receiving external measurements, thus exposing their navigation solution to drift. Over the years, a wide variety of works have been proposed to overcome this shortcoming, by exploiting knowledge of the system current conditions and turning it into an applicable source of information to update the navigation filter. This paper aims to provide an extensive survey of information aided navigation, broadly classified into direct, indirect, and model aiding. Each approach is described by the notable works that implemented its concept, use cases, relevant state updates, and their corresponding measurement models. By matching the appropriate constraint to a given scenario, one will be able to improve the navigation solution accuracy, compensate for the lost information, and uncover certain internal states, that would otherwise remain unobservable.Comment: 8 figures, 3 table