Self-supervised learning method for unstructured road detection using Fuzzy Support Vector Machines

Zhou, Shengyan; Iagnemma, Karl

doi:10.1109/iros.2010.5650300

Cited by 26 publications

(2 citation statements)

References 13 publications

(14 reference statements)

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“…Self-supervised learning (SSL): In SSL, labels are automatically generated from data rather than from annotation. SSL is a broad topic and has been used in offline settings with applications including monocular road detection [23], [24], [25], terrain traversability [26], and general representation learning [27], [28]. It has also been used to adapt semantic segmentation networks to out-of-distribution data by enforcing augmentation consistency via a momentum network [29] which we leverage in our work.…”

Section: Related Workmentioning

confidence: 99%

Online self-supervised monocular visual odometry for ground vehicles

Lee

Daniilidis

Lee

2015

2015 IEEE International Conference on Robotics and Automation (ICRA)

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This paper presents an online self-supervised approach to monocular visual odometry and ground classification applied to ground vehicles. We solve the motion and structure problem based on a constrained kinematic model. The true scale of the monocular scene is recovered by estimating the ground surface. We consider a general parametric ground surface model and use the Random Sample Consensus (RANSAC) algorithm for robust fitting of the parameters. The estimated ground surface provides training samples to learn a probabilistic appearance-based ground classifier in an online and selfsupervised manner. The appearance-based classifier is then used to bias the RANSAC sampling to generate better hypotheses for parameter estimation of the ground surface model. Thus, without relying on any prior information, we combine geometric estimates with appearance-based classification to achieve an online self-learning scheme from monocular vision. Experimental results demonstrate that online learning improves the computational efficiency and accuracy compared to standard sampling in RANSAC. Evaluations on the KITTI benchmark dataset demonstrate the stability and accuracy of our overall methods in comparison to previous approaches. a We define ground as simply the lowest portion of the 3D scene where the wheels of ground vehicle would contact.

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Section: Related Workmentioning

confidence: 99%

Online self-supervised monocular visual odometry for ground vehicles

Lee

Daniilidis

Lee

2015

2015 IEEE International Conference on Robotics and Automation (ICRA)

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“…Regardless of this achievement, environmental noise such as rain and/or snow can cause misdetection of drivable path which can lead to autonomous driving system accident. This is because environmental noises have the capability to affect the color properties of the image with significant effects of misclassification of road as non-road and vice versa [29]. This paper makes a number of fundamental contributions to removing real rain effects from images.…”

Section: Introductionmentioning

confidence: 99%

Removal of Visual Disruption Caused by Rain Using Cycle-Consistent Generative Adversarial Networks

Tang

Lim

Siebert

2019

Lecture Notes in Computer Science

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This paper addresses the problem of removing rain disruption from images for outdoor vision systems. The Cycle-Consistent Generative Adversarial Network (CycleGAN) is proposed as a more promising rain removal algorithm, as compared to the state-of-the-art Image Deraining Conditional Generative Adversarial Network (ID-CGAN). The CycleGAN has an advantage in its ability to learn the underlying relationship between the rain and rain-free domain without the need of paired domain examples. Based on rain physical properties and its various phenomena, five broad categories of real rain distortions are proposed in this paper. For a fair comparison, both networks were trained on the same set of synthesized rain-and-ground-truth image-pairs provided by the ID-CGAN work, and subsequently tested on real rain images which fall broadly under these five categories. The comparison results demonstrated that the CycleGAN is superior in removing real rain distortions.

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