INS-Aided Odometry and Laser Scanning Data Integration for Real Time Positioning and Map-Building of Skid-Steered Vehicles (original) (raw)
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A Dead-reckoning Scheme for Skid-steered Vehicles in Outdoor Environments
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A dead-reckoning scheme appropriate for skidsteered mobile robots is introduced to serve in a wider scheme for simultaneous localization and map-building. It is based on internal sensors (inertial data and odometry) only. The information from an experimentally derived kinematic model and an onboard Inertial Navigation System (INS), after a necessary pre-filtering stage, is fused using a simple and fast modified Kalman filter. We verify our approach with large scale experiments in outdoors structured environments and variable terrains, following paths with steep turns and variable velocity.
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It is expected that an Inertial Navigation System (INS), when used on a land vehicle, would provide a superior position estimate to that which would be determined using odometry. There are, however, a number of benefits to using odometry as opposed to an INS technique, including high physical robustness and reliability of the encoders, low weight, power consumption and cost, and a zero time-dependent drift. For these points alone, it would seem logical to include some form of odometry on any autonomous surface vehicle. If this last point is true, and an INS technique is to be implemented, then it follows that an attempt should be made to improve the overall system performance by combining INS and odometry signals into an optimal, or at least more robust, estimation system.
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This work evaluates the impact of two state-of-the-art aiding techniques to enhance the performance of inertial navigation systems (INS). A new embedded methodology to integrate the vehicle dynamics (VD) in the navigation system is proposed, by modeling it directly in the Extended Kalman Filter. The embedded VD and the INS algorithm prop- agate simultaneously the inertial states, allowing for the
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Many algorithms related to localization need good pose prediction in order to produce accurate results. This is especially the case for data association algorithms, where false feature matches can lead to the localization system failure. In rough terrain, the field of view can vary significantly between two feature extraction steps, so a good position prediction is necessary to robustly track features. This paper presents a method for combining dead reckoning sensor information in order to provide an initial estimate of the six degrees of freedom of a rough terrain rover. An inertial navigation system (INS) and the wheel encoders are used as sensory inputs. The sensor fusion scheme is based on an extended information filter (EIF) and is extensible to any kind and number of sensors. In order to test the system, the rover has been driven on different kind of obstacles while computing both pure 3D-odometric and fused INS/3Dodometry trajectories. The results show that the use of the INS significantly improves the pose prediction.
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Simultaneous localization and map building of skid-steered robots
IEEE Robotics & Automation Magazine, 2000
... propose an alternative scheme, where the state vector holds only the robot pose and the map is feature-based in the ... Laser Scanner Data ... Matching This section describes how the robot pose estimate is obtained by extending a map matching and scan-matching algorithm [23 ...
Multi-aided Inertial Navigation for Ground Vehicles in Outdoor Uneven Environments
2005
A good localization ability is essential for an autonomous vehicle to perform any functions. For ground vehicles operating in outdoor, uneven and unstructured environments, the localization task becomes much more difficult than in indoor environments. In urban or forest environments where high buildings or tall trees exist, GPS sensors also fail easily. The main contribution of this paper is that a multi-aided inertial based localization system has been developed to solve the outdoor localization problem. The multi-aiding information is from odometry, an accurate gyroscope and vehicle constraints. Contrary to previous work, a kinematic model is developed to estimate the inertial sensor’s lateral velocity. This is particularly important when cornering at speed, and side slip occurs. Experimental results are presented of this system which is able to provide a vehicle’s position, velocity and attitude estimation accurately, even when the testing vehicle runs in outdoor uneven environments.
Navigation, 2007
The paper describes a fully autonomous relative navigation solution for urban environments (indoor and outdoor). The navigation solution is derived by combining measurements from a two-dimensional (2D) laser scanner with measurements from inertial sensors. Navigation relies on the availability of structures (lines and surfaces) within the scan range (80 m, typically). Features (e.g. lines and corners) are first extracted from 2D laser scans and then used for position and heading determination. Inertial navigation states (position, velocity and attitude) are applied to predict feature displacements between laser scans, computationally adjust a 2D scan plane for tilting of the scanner platform, and to coast through scans where features extracted from scan images do not provide sufficient information for navigation. Parameters of features extracted from scan images are applied to periodically re-calibrate inertial states in order to reduce the error drift of inertial navigation parameters. The calibration herein uses a Kalman filter that performs a range-domain fusion of laser scanner and inertial data. Indoor and outdoor live data test results are used to demonstrate performance characteristics of the laser/inertial integrated navigation. Positioning at the cm-level is demonstrated for indoor scenarios where well-defined features and good feature geometry are available. Test data from challenging urban environments show position errors at the meter-level after approx. 200 m of travel (between 0.6% and 0.8% of distance travelled). Soloviev, A.; Bates, D.; van Graas, F. (2007) Tight Coupling of Laser Scanner and Inertial Measurements for a Fully Autonomous Relative Navigation Solution. In Military Capabilities Enabled by Advances in Navigation Sensors (pp. 4-1-4-30). Meeting Proceedings RTO-MP-SET-104, Paper 4. Neuilly-sur-Seine, France: RTO. Available from: http://www.rto.nato.int. Tight Coupling of Laser Scanner and Inertial Measurements for a Fully Autonomous Relative Navigation Solution UNCLASSIFIED/UNLIMITED A laser scanner is employed to detect features that are associated with man-made obstacles. The scanner measures distances to reflecting surrounding objects in a certain angular range. Scanner operations are based on a time-of-flight measurement principle. The laser scanner sends out a pulse which returns to the scanner if reflected by an object in the scanner measurement range. The time-of-flight recorded is applied to estimate the distance to a reflecting object. The use of a rotating mirror allows for capturing object reflections in a certain angular range. Figure 1 illustrates operations of a two-dimensional (2D) laser scanner. Reflecting object Laser scanner Measurement range Angular range 4-28 RTO-MP-SET-104 UNCLASSIFIED/UNLIMITED Tight Coupling of Laser Scanner and Inertial Measurements for a Fully Autonomous Relative Navigation Solution UNCLASSIFIED/UNLIMITED
Sensors, 2015
Skid-steering mobile robots are widely used because of their simple mechanism and robustness. However, due to the complex wheel-ground interactions and the kinematic constraints, it is a challenge to understand the kinematics and dynamics of such a robotic platform. In this paper, we develop an analysis and experimental kinematic scheme for a skid-steering wheeled vehicle based-on a laser scanner sensor. The kinematics model is established based on the boundedness of the instantaneous centers of rotation (ICR) of treads on the 2D motion plane. The kinematic parameters (the ICR coefficient , the path curvature variable and robot speed), including the effect of vehicle dynamics, are introduced to describe the kinematics model. Then, an exact but costly dynamic model is used and the simulation of this model's stationary response for the vehicle shows a qualitative relationship for the specified parameters and. Moreover, the parameters of the kinematic model are determined based-on a laser scanner localization experimental analysis method with a skid-steering robotic platform, Pioneer P3-AT. The relationship between the ICR coefficient and two physical factors is studied, i.e., the radius of the path curvature and the robot speed. An empirical function-based relationship between the ICR coefficient of the robot and the path parameters is derived. To validate the obtained results, it is empirically demonstrated that the proposed kinematics model significantly improves the dead-reckoning performance of this skid-steering robot.