Robust Feature Matching Based on Adaptive ORB for Vision-based Robot Navigation

Sun, Chao; Qiao, Nianzu; Sun, Jia

doi:10.1109/yac53711.2021.9486546

Cited by 2 publications

(1 citation statement)

References 17 publications

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“…To ensure scale invariances, a Gaussian pyramid and patch centroid calculation based on FAST are combined in ORB. Moreover, an improvement on the BRIEF algorithm for rotation invariance is designed for constructing feature descriptors of ORB [21][22][23].…”

Section: Orb Briefmentioning

confidence: 99%

Fast Automatic Registration of UAV Images via Bidirectional Matching

Luo,

Wei,

Jin

et al. 2023

Sensors

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Image registration plays a vital role in the mosaic process of multiple UAV (Unmanned Aerial Vehicle) images acquired from different spatial positions of the same scene. Aimed at the problem that many fast registration methods cannot provide both high speed and accuracy simultaneously for UAV visible light images, this work proposes a novel registration framework based on a popular baseline registration algorithm, ORB—the Oriented FAST (Features from Accelerated Segment Test) and Rotated BRIEF (Binary Robust Independent Elemental Features) algorithm. First, the ORB algorithm is utilized to extract image feature points fast. On this basis, two bidirectional matching strategies are presented to match obtained feature points. Then, the PROSRC (Progressive Sample Consensus) algorithm is applied to remove false matches. Finally, the experiments are carried out on UAV image pairs about different scenes including urban, road, building, farmland, and forest. Compared with the original version and other state-of-the-art registration methods, the bi-matching ORB algorithm exhibits higher accuracy and faster speed without any training or prior knowledge. Meanwhile, its complexity is quite low for on-board realization.

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Section: Orb Briefmentioning

confidence: 99%

Fast Automatic Registration of UAV Images via Bidirectional Matching

Luo,

Wei,

Jin

et al. 2023

Sensors

View full text Add to dashboard Cite

show abstract

A Review of Brain-Inspired Cognition and Navigation Technology for Mobile Robots

Bai,

Shao,

Zhang

et al. 2024

Cyborg Bionic Syst

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Brain-inspired navigation technologies combine environmental perception, spatial cognition, and target navigation to create a comprehensive navigation research system. Researchers have used various sensors to gather environmental data and enhance environmental perception using multimodal information fusion. In spatial cognition, a neural network model is used to simulate the navigation mechanism of the animal brain and to construct an environmental cognition map. However, existing models face challenges in achieving high navigation success rate and efficiency. In addition, the limited incorporation of navigation mechanisms borrowed from animal brains necessitates further exploration. On the basis of the brain-inspired navigation process, this paper launched a systematic study on brain-inspired environment perception, brain-inspired spatial cognition, and goal-based navigation in brain-inspired navigation, which provides a new classification of brain-inspired cognition and navigation techniques and a theoretical basis for subsequent experimental studies. In the future, brain-inspired navigation technology should learn from more perfect brain-inspired mechanisms to improve its generalization ability and be simultaneously applied to large-scale distributed intelligent body cluster navigation. The multidisciplinary nature of brain-inspired navigation technology presents challenges, and multidisciplinary scholars must cooperate to promote the development of this technology.

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Robust Feature Matching Based on Adaptive ORB for Vision-based Robot Navigation

Cited by 2 publications

References 17 publications

Fast Automatic Registration of UAV Images via Bidirectional Matching

Fast Automatic Registration of UAV Images via Bidirectional Matching

A Review of Brain-Inspired Cognition and Navigation Technology for Mobile Robots

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