Monocular template-based vehicle tracking for autonomous convoy driving

Fries, Carsten; Wuensche, Hans-Joachim

doi:10.1109/iros.2014.6942935

Cited by 9 publications

(3 citation statements)

References 26 publications

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“…Other related works in vision-based convoying often employ template-based methods with fiducial markers placed on the leading agent [1], [2]. Such methods match the template to the image to calculate the estimated pose of the leading robot.…”

Section: A Vision-based Convoyingmentioning

confidence: 99%

“…Vision-based tracking solutions have been applied to robot convoying in a variety of contexts, including terrestrial driving [1], [2], on-rails maintenance vehicles [3], and unmanned aerial vehicles [4]. Our work demonstrates robust tracking and detection in challenging underwater settings which are employed successfully for underwater convoying in ocean experiments.…”

Section: Introductionmentioning

confidence: 98%

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Underwater multi-robot convoying using visual tracking by detection

Shkurti

Chang

Henderson

et al. 2017

2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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Abstract-We present a robust multi-robot convoying approach that relies on visual detection of the leading agent, thus enabling target following in unstructured 3-D environments. Our method is based on the idea of tracking-by-detection, which interleaves efficient model-based object detection with temporal filtering of image-based bounding box estimation. This approach has the important advantage of mitigating tracking drift (i.e. drifting away from the target object), which is a common symptom of model-free trackers and is detrimental to sustained convoying in practice. To illustrate our solution, we collected extensive footage of an underwater robot in ocean settings, and hand-annotated its location in each frame. Based on this dataset, we present an empirical comparison of multiple tracker variants, including the use of several convolutional neural networks, both with and without recurrent connections, as well as frequency-based model-free trackers. We also demonstrate the practicality of this tracking-by-detection strategy in real-world scenarios by successfully controlling a legged underwater robot in five degrees of freedom to follow another robot's independent motion.

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Section: A Vision-based Convoyingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 98%

Underwater multi-robot convoying using visual tracking by detection

Shkurti

Chang

Henderson

et al. 2017

2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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“…our fast templatebased vehicle detection algorithm [4], [8], we initialize both filters with this data. Otherwise we set λ = 0, v to the ego velocity and the relative pose P ego→obj = {r, α, h, ψ, θ, φ} to {10, 0, 0, 0, 0, 0}.…”

Section: A Filter Initializationmentioning

confidence: 99%

Autonomous convoy driving by night: The vehicle tracking system

Fries

Wuensche

2015

2015 IEEE International Conference on Technologies for Practical Robot Applications (TePRA)

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Previous publications of our institute describe a robust vehicle tracking system for daylight conditions. This paper presents an improved vehicle tracking system which is able to detect and track the convoy leader also by twilight and night. The primary sensor equipment consists of a daytime camera, a LiDAR and an inertial navigation system. An expansion with a thermal and a lowlight camera was necessary to be robust against any illumination conditions. The system is capable of estimating the relative 3D position and orientation, the velocity and the steering angle of a convoy leader precisely in real-time. This makes it possible to follow the convoy leader's track. Another novelty is coupling a Kalman filter with a particle filter for higher stability and accuracy in vehicle tracking. The tracking system shows excellent functionality while driving more than 50 km fully autonomously in urban-and unstructured environments at night.978-1-4799-8757-3/15/$31.00 ©2015 IEEE

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