Robust Omnidirectional Vision based Mobile Robot Hierarchical Localization and Autonomous Navigation

Li, Maohai; Sun, Lining; Huang, Qingcheng; Cai, Zixing; Piao, Songhao

doi:10.3923/itj.2011.29.39

Cited by 41 publications

(6 citation statements)

References 10 publications

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“…al. performed a two-stage localization hierarchy, using a color histogram global image descriptor to provide a coarse localization and then evaluated the resultant candidates using SIFT feature matching [8]. A more advanced version of this was developed which uses the PHOG descriptor at the first stage of the hierarchy, then uses FAST corners with LDB binary descriptors [6].…”

Section: Related Workmentioning

confidence: 99%

Hierarchical Multi-Process Fusion for Visual Place Recognition

Hausler

Milford

2020

2020 IEEE International Conference on Robotics and Automation (ICRA)

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A recent approach to the Visual Place Recognition (VPR) problem has been to fuse the place recognition estimates of multiple complementary VPR techniques simultaneously. However, selecting the optimal set of techniques to use in a specific deployment environment a-priori is a difficult and unresolved challenge. Further, to the best of our knowledge, no method exists which can select a set of techniques on a frame-by-frame basis in response to image-to-image variations. In this work, we propose an unsupervised algorithm that finds the most robust set of VPR techniques to use in the current deployment environment, on a frame-by-frame basis. The selection of techniques is determined by an analysis of the similarity scores between the current query image and the collection of database images and does not require ground-truth information. We demonstrate our approach on a wide variety of datasets and VPR techniques and show that the proposed dynamic multi-process fusion (Dyn-MPF) has superior VPR performance compared to a variety of challenging competitive methods, some of which are given an unfair advantage through access to the ground-truth information.

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Section: Related Workmentioning

confidence: 99%

Hierarchical Multi-Process Fusion for Visual Place Recognition

Hausler

Milford

2020

2020 IEEE International Conference on Robotics and Automation (ICRA)

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“…Maohai et al [32] studied a strong robust hierarchical localization method, and realized a coarse-to-fine hierarchical localization and autonomous navigation system for mobile robot based on pure vision. Emilio et al [33] proposed an appearance-based method for topological mapping based on hierarchical decomposition of environment, and proved that the hierarchical method could reduce search space in identifying place while improve mapping accuracy in creating a map.…”

Section: Introductionmentioning

confidence: 99%

Hierarchical Visual Place Recognition Based on Semantic-Aggregation

et al. 2021

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A major challenge in place recognition is to be robust against viewpoint changes and appearance changes caused by self and environmental variations. Humans achieve this by recognizing objects and their relationships in the scene under different conditions. Inspired by this, we propose a hierarchical visual place recognition pipeline based on semantic-aggregation and scene understanding for the images. The pipeline contains coarse matching and fine matching. Semantic-aggregation happens in residual aggregation of visual information and semantic information in coarse matching, and semantic association of semantic edges in fine matching. Through the above two processes, we realized a robust coarse-to-fine pipeline of visual place recognition across viewpoint and condition variations. Experimental results on the benchmark datasets show that our method performs better than several state-of-the-art methods, improving the robustness against severe viewpoint changes and appearance changes while maintaining good matching-time performance. Moreover, we prove that it is possible for a computer to realize place recognition based on scene understanding.

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“…Among the applications of stereovision found the construction of map, 3D reconstruction (Khalil et al, 2010), face recognition, 3D motion (Wedel et al, 2010); robot navigation where omnidirectional images is often used (Maohai et al, 2011). The omnidirectional vision is a vision process that provides a sphere of sight of the world observed from its center.…”

Section: Introductionmentioning

confidence: 99%

Improvement of Image Matching by using the Proximity Criterion: Application to Omnidirectional and Perspective Images

Guelzim¹,

Hammouch²,

Aboutajdine³

2011

Journal of Computer Science

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Problem statement: In computer vision, matching is an important phase for several applications (object reconstruction, robot navigation ...). The similarity measures used provided results which could be improved. Approach: This research proposed to improve image matching by using the proximity criterion. The similarity measures used mutual information and correlation coefficient. The matching was done between neighborhoods of points of interest extracted from the images. The second chance algorithm was also applied. We have worked in case which the sensor had a slight displacement between two images. The tests were performed on omnidirectional and perspective grayscale images. Results: The improvement by introducing the proximity criterion reached 15.9% for non-noised perspective images, 32.1% for noised perspective images, 47.69% for non-noised omnidirectional images and 58.5% for noised omnidirectional images. Conclusion/Recommendations: The introduction of the proximity criterion has significantly improved the performance of the matching. The method is recommended in mobile robotics, knowing that a good matching leads to a better location and better movement of the robot

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Robust Omnidirectional Vision based Mobile Robot Hierarchical Localization and Autonomous Navigation

Cited by 41 publications

References 10 publications

Hierarchical Multi-Process Fusion for Visual Place Recognition

Hierarchical Multi-Process Fusion for Visual Place Recognition

Hierarchical Visual Place Recognition Based on Semantic-Aggregation

Improvement of Image Matching by using the Proximity Criterion: Application to Omnidirectional and Perspective Images

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