Optimum synthesis of the four-bar function generator in its symmetric embodiment: the Ackermann steering linkage

Simionescu, P. A.; Beale, David G.

doi:10.1016/s0094-114x(02)00071-x

Cited by 76 publications

(52 citation statements)

References 7 publications

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“…Among the works on VO, those exploiting the vehicle kinematics are relevant for this paper. Scaramuzza et al [13], [14] leveraged the Ackerman-steering principle [15] to approximate the vehicle motion as locallyplanar and circular and showed that this allows parametrizing the car motion in terms of a single feature correspondence. This led to very efficient algorithms for structure from motion, such as 1-point RANSAC or histogram voting.…”

Section: A Related Workmentioning

confidence: 99%

Multi-body Motion Estimation from Monocular Vehicle-Mounted Cameras

Sabzevari

Scaramuzza

2016

IEEE Trans. Robot.

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This paper addresses the problem of simultaneous estimation of the vehicle ego-motion and motions of multiple moving objects in the scene-called eoru-motions-through a monocular vehiclemounted camera. Localization of multiple moving objects and estimation of their motions is crucial for autonomous vehicles. Conventional localization and mapping techniques (e.g. Visual Odometry and SLAM) can only estimate the ego-motion of the vehicle. The capability of robot localization pipeline to deal with multiple motions has not been widely investigated in the literature. We present a theoretical framework for robust estimation of multiple relative motions in addition to the camera ego-motion. First, the framework for general unconstrained motion is introduced and then, it is adapted to exploit the vehicle kinematic constraints to increase efficiency. The method is based on projective factorization of the multiple-trajectory matrix. First, the ego-motion is segmented and, then, several hypotheses are generated for the eoru-motions. All the hypotheses are evaluated and the one with the smallest reprojection error is selected. The proposed framework does not need any a priori knowledge of the number of motions and is robust to noisy image measurements. The method with constrained motion model is evaluated on a popular street-level image dataset collected in urban environments (KITTI dataset) including several relative ego-motion and eoru-motion scenarios. A benchmark dataset (Hopkins 155) is used to evaluate this method with general motion model. The results are compared with those of the state-of-the-art methods considering a similar problem, referred to as the Multi-Body Structure from Motion in the computer vision community. Abstract-This paper addresses the problem of simultaneous estimation of the vehicle ego-motion and motions of multiple moving objects in the scene-called eoru-motions-through a monocular vehicle-mounted camera. Localization of multiple moving objects and estimation of their motions is crucial for autonomous vehicles. Conventional localization and mapping techniques (e.g. Visual Odometry and SLAM) can only estimate the ego-motion of the vehicle. The capability of robot localization pipeline to deal with multiple motions has not been widely investigated in the literature. We present a theoretical framework for robust estimation of multiple relative motions in addition to the camera ego-motion. First, the framework for general unconstrained motion is introduced and then, it is adapted to exploit the vehicle kinematic constraints to increase efficiency. The method is based on projective factorization of the multiple-trajectory matrix. First, the ego-motion is segmented and, then, several hypotheses are generated for the eoru-motions. All the hypotheses are evaluated and the one with the smallest reprojection error is selected. The proposed framework does not need any a priori knowledge of the number of motions and is robust to noisy image measurements. The method with constrained motion model is ...

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Section: A Related Workmentioning

confidence: 99%

Multi-body Motion Estimation from Monocular Vehicle-Mounted Cameras

Sabzevari

Scaramuzza

2016

IEEE Trans. Robot.

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“…A fischertechnik servo was used to drive the tie rod, with a slot used to provide the necessary degree of freedom. Because it has little influence over practical values [9], effect of the knuckle length was not specifically addressed. The single wheel drive was applied to the left rear wheel.…”

Section: Chassis Base Layoutmentioning

confidence: 99%

A CAD Based Concept Car Workflow

Spence

2010

PCEEA

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“…The main parameters of the Ackermann linkage, approximated for convenience as planar linkage, will be determined using the design charts available in [7]. An analysis of the mechanism with nonparallel kingpin axes will be further conducted to check the accuracy of the results.…”

Section: Ackermann Linkage Designmentioning

confidence: 99%

“…1). The Ackermann linkage has been investigated by a number of authors in the past [3][4][5][6][7][8]. It is a symmetric, four-bar spatial function generator symbolized RSSR (S stands from spherical joint and R from rotational or turning joint), and has the role of pivoting the steered wheels at certain variable ratio imposed by the condition of correct turning i.e.…”

Section: Introductionmentioning

confidence: 99%