Data association and map management for robot SLAM using local invariant features

Wang, Yin-Tien; Feng, Ying-Chieh

doi:10.1109/icma.2013.6618068

Cited by 3 publications

(2 citation statements)

References 14 publications

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“…It was mainly improved to deal with the disadvantages of the slow operation speed and extensive computation of the SIFT algorithm. The SURF feature is widely used as a feature extraction and analysis method for V-SLAM [86,93,94], and the algorithm flow is shown in Figure 32. Firstly, a Gaussian pyramid scale-space needs to be constructed.…”

Section: Feature Detection and Matchingmentioning

confidence: 99%

Visual-SLAM Classical Framework and Key Techniques: A Review

Jia

Zhang

et al. 2022

Sensors

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With the significant increase in demand for artificial intelligence, environmental map reconstruction has become a research hotspot for obstacle avoidance navigation, unmanned operations, and virtual reality. The quality of the map plays a vital role in positioning, path planning, and obstacle avoidance. This review starts with the development of SLAM (Simultaneous Localization and Mapping) and proceeds to a review of V-SLAM (Visual-SLAM) from its proposal to the present, with a summary of its historical milestones. In this context, the five parts of the classic V-SLAM framework—visual sensor, visual odometer, backend optimization, loop detection, and mapping—are explained separately. Meanwhile, the details of the latest methods are shown; VI-SLAM (Visual inertial SLAM) is reviewed and extended. The four critical techniques of V-SLAM and its technical difficulties are summarized as feature detection and matching, selection of keyframes, uncertainty technology, and expression of maps. Finally, the development direction and needs of the V-SLAM field are proposed.

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Section: Feature Detection and Matchingmentioning

confidence: 99%

Visual-SLAM Classical Framework and Key Techniques: A Review

Jia

Zhang

et al. 2022

Sensors

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“…SURF features have scale invariance, rotation invariance, and the speed of the algorithm relative to the SIFT feature is increased by 3 to 7 times. In [9,10,11], SURF was used for the slam system, compared with the SIFT characteristics, the time complexity is reduced. The Euclidean distance between two feature vectors is usually calculated when matching the SIFT and SURF features of two images, which is used as the similarity measure of feature points.…”

Section: Related Workmentioning

confidence: 99%

A Micro SLAM System Based on ORB for RGB-D Cameras

et al. 2018

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In this paper, a micro SLAM system based on ORB features for RGB-D cameras has been proposed. With only a RGB-D sensor, this method can be applied in small environment for localization and mapping. Furthermore, the task of 3D reconstruction can also be accomplished by using the approach. The pose graph based on Bundle Adjustment is adopted for reducing the estimation error. In order to further speed up computing to meet the requirement of real-time, we have proposed the piecewise optimization strategy. The approach is evaluated on public benchmark datasets. Compared with several state-of-the-art scheme, this method has proven to work well in these environments.

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Research of SLAM data association algorithm

Zhang

Feng

et al. 2018

Proceedings of the 2nd International Conference on Innovation in Artificial Intelligence

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Data association and map management for robot SLAM using local invariant features

Cited by 3 publications

References 14 publications

Visual-SLAM Classical Framework and Key Techniques: A Review

Visual-SLAM Classical Framework and Key Techniques: A Review

A Micro SLAM System Based on ORB for RGB-D Cameras

Research of SLAM data association algorithm

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