Trail following with omnidirectional vision

Rasmussen, Christopher; Lu, Yan; Kocamaz, Mehmet Kemal

doi:10.1109/iros.2010.5650189

Cited by 7 publications

(3 citation statements)

References 26 publications

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“…The problem of obstacle detection has been explicitly or implicitly addressed previously in the field of unmanned ground vehicles (UGV). In a trail-following application Rasmussen et al [2] use an omnidirectional camera to detect arXiv:1503.01918v1 [cs.CV] 6 Mar 2015…”

Section: Related Workmentioning

confidence: 99%

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Fast Image-Based Obstacle Detection From Unmanned Surface Vehicles

Kristan

Kenk

Kovačič

et al. 2016

IEEE Trans. Cybern.

128

156

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Abstract-Obstacle detection plays an important role in unmanned surface vehicles (USV). The USVs operate in highly diverse environments in which an obstacle may be a floating piece of wood, a scuba diver, a pier, or a part of a shoreline, which presents a significant challenge to continuous detection from images taken onboard. This paper addresses the problem of online detection by constrained unsupervised segmentation. To this end, a new graphical model is proposed that affords a fast and continuous obstacle image-map estimation from a single video stream captured onboard a USV. The model accounts for the semantic structure of marine environment as observed from USV by imposing weak structural constraints. A Markov random field framework is adopted and a highly efficient algorithm for simultaneous optimization of model parameters and segmentation mask estimation is derived. Our approach does not require computationally intensive extraction of texture features and comfortably runs in real-time. The algorithm is tested on a new, challenging, dataset for segmentation and obstacle detection in marine environments, which is the largest annotated dataset of its kind. Results on this dataset show that our model outperforms the related approaches, while requiring a fraction of computational effort.

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Section: Related Workmentioning

confidence: 99%

“…The Modd consists of 12 video sequences, providing in total 4454 fully annotated frames with resolution of 640 x 480 pixels. The dataset is made publicly available along with the annotations and Matlab evaluation routines from the MODD homepage 2 .…”

Section: Marine Obstacle Detection Datasetmentioning

confidence: 99%

Fast Image-Based Obstacle Detection From Unmanned Surface Vehicles

Kristan

Kenk

Kovačič

et al. 2016

IEEE Trans. Cybern.

128

156

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“…Also, trees are important in outdoor robot navigation both as obstacles and potential landmarks. In related work [3,4], we have studied robotic hiking and mountain-bike trail following in a variety of terrain types based on color appearance contrast. In forested terrain, trees are of course the most prominent obstacles affecting motion planning, and we would also like to carry out the above classification and modeling tasks autonomously.…”

Section: Introductionmentioning

confidence: 99%

Tree trunk detection using contrast templates

Rasmussen

2011

2011 18th IEEE International Conference on Image Processing

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We propose a simple contrast-based method for tree detection and shape estimation from ground-plane perspective images for purposes of counting, classification, modeling, or robotic obstacle avoidance. Under the assumption that tree trunks are relatively narrow and vertical shapes which strongly differ in appearance from the scene background and have boundaries of opposite contrast, we apply a bank of bar filters parametrized from camera intrinsics combined with trunk location and diameter limits, and integrate results vertically. Nonmaximum suppression is applied to candidates in the resulting trunk likelihood image. We present results demonstrating the effectiveness of our tree detection algorithm on a variety of forested images obtained directly from our robot as well as sampled from the web, and quantify performance using ground truth on a subset of those images. We also compare the results of our method with several related published approaches.

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