A real-time, multi-sensor architecture for fusion of delayed observations: application to vehicle localization

Tessier, C.; Cariou, Christophe; Debain, Christophe; Chausse, Frédéric; Chapuis, Roland; Rousset, Cedric

doi:10.1109/itsc.2006.1707405

Cited by 30 publications

(19 citation statements)

References 8 publications

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“…The same GPS receiver as on the tractor is used and also is installed straight up the center of the rear axle. An architecture based on a controller area network (CAN) bus has been developed, as well as a software framework managing unsynchronized sensors and delayed observations; see Tessier et al (2006). Two electric jacks allow control of the steering of the front and rear wheels, equipped with odometers.…”

Section: Experimental Platformsmentioning

confidence: 99%

Automatic guidance of a four‐wheel‐steering mobile robot for accurate field operations

Cariou¹,

Lenain²,

Thuilot³

et al. 2009

Journal of Field Robotics

107

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As world population growth requires an increasing level of farm production at the same time that environmental preservation is a priority, the development of new agricultural tools and methods is required. In this framework, the development of robotic devices can provide an attractive solution, particularly in the field of autonomous vehicles. Accurate automatic guidance of mobile robots in farming constitutes a challenging problem for researchers, mainly due to the low grip conditions usually found in such a context. From assisted-steering systems to agricultural robotics, numerous control algorithms have been studied to achieve high-precision path tracking and have reached an accuracy within ±10 cm, whatever the ground configuration and the path to be followed. However, most existing approaches consider classical two-wheel-steering vehicles. Unfortunately, by using such a steering system, only the lateral deviation with respect to the path to be followed can be satisfactorily controlled. Indeed, the heading of the vehicle remains dependent on the grip conditions, and crabwise motions, for example, are systematically observed on a slippery slope, leading to inaccurate field operations. To tackle this drawback, a four-wheel-steering (4WS) mobile robot is considered, enabling servo of both Cariou et al.: Automatic Guidance of a Four-Wheel-Steering Mobile Robot • 505lateral and angular deviations with respect to a desired trajectory. The path tracking control is designed using an extended kinematic representation, allowing account to be taken online of wheel skidding, while a backstepping approach permits management of the 4WS structure. The result is an approach taking advantage of both rear and front steering actuations to fully compensate for sliding effects during path tracking. Moreover, a predictive algorithm is developed in order to address delays induced by steering actuators, compensating for transient overshoots in curves. Experimental results demonstrate that despite sliding phenomena, the mobile robot is able to automatically and accurately achieve a desired path, with lateral and angular errors, respectively, within ±10 cm and ±2 deg, whatever its shape and whatever the terrain conditions. This constitutes a promising result in efforts to define efficient tools with which to tackle tomorrow's agriculture challenge. C 2009 Wiley Periodicals, Inc.

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Section: Experimental Platformsmentioning

confidence: 99%

Automatic guidance of a four‐wheel‐steering mobile robot for accurate field operations

Cariou¹,

Lenain²,

Thuilot³

et al. 2009

Journal of Field Robotics

107

View full text Add to dashboard Cite

show abstract

“…The problem is that the data from the rangefinder are not synchronized with the camera's pictures (see fig.2) Thanks to the previous work in our lab. [17] we can recover an optimal state vector at the desired date. In this case we use a spline interpolation of the 6 parameters of the localization to retrieve the camera position and orientation at the rangefinder date.…”

Section: Unsynchronized Datamentioning

confidence: 99%

“…All those results in this paper have been processed in realtime with a C++ software [17]. The computation time in regards to the number of points is represented in Fig.…”

Section: B Slam Localization Evaluationmentioning

confidence: 99%

Real-time dense digital elevation map estimation using laserscanner and camera SLAM process

Malartre¹,

Delmas²,

Chapuis³

et al. 2010

2010 11th International Conference on Control Automation Robotics &Amp; Vision

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This paper is about environment perception for navigation system in outdoor applications. Unlike other approaches that try to detect an obstacle of binary state, we consider here a Digital Elevation Map (DEM). This map has to be built in regards to the guidance system's needs. These needs depend on the vehicle capabilities, its dynamics constraints, its speed etc... Starting with the navigation system's needs, our goal is to estimate a precise and dense DEM. Our approach is based on a SLAM algorithm combining a 2D rangefinder and a camera. Thus we can estimate both the displacement between two laser scan and have a good density of the reconstructed DEM. The approach has been validated both using simulated realistic data and real data in real-time on our robot in outdoor environment (approx. 40ms per loop).

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“…Proprioceptive sensors are connected to the low-level computer which sends measurements to the second computer called high-level through an ethernet link. The vehicle localization algorithms (vision based and fusion) are implemented on the high-level computer in C++ language using the library AROCCAM presented in [14].…”

Section: B Control Law Designmentioning

confidence: 99%

Improved visual localization and navigation using proprioceptive sensors

Karam

Hadj-Abdelkader

Deymier

et al. 2010

2010 IEEE/RSJ International Conference on Intelligent Robots and Systems

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Projet VIPAInternational audienceWe address the problem of vehicle (mobile robot) navigation by combining visual-based reconstruction and localization with metrical information given by the proprioceptive sensors such as the odometry sensor. The proposed approach extends the navigation system based on a monocular vision [1] which is able to build a map and localize the vehicle in the real time way using only one camera. An extended kalman filter is used to integrate odometric information to estimate the vehicle position. This position is updated by the localization obtained from the vision system. Experimental result carried out with an urban electric vehicle will show the improvement of the navigation system and its robustness to the temporary loss of images

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A real-time, multi-sensor architecture for fusion of delayed observations: application to vehicle localization

Cited by 30 publications

References 8 publications

Automatic guidance of a four‐wheel‐steering mobile robot for accurate field operations

Automatic guidance of a four‐wheel‐steering mobile robot for accurate field operations

Real-time dense digital elevation map estimation using laserscanner and camera SLAM process

Improved visual localization and navigation using proprioceptive sensors

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