3D Reconstruction of an Indoor Environment Using SLAM with Modified SURF and A-KAZE Feature Extraction Algorithm

Srividhya, S.; Prakash, Surya; Elangovan, K.

doi:10.1007/978-3-030-30465-2_16

Cited by 3 publications

(2 citation statements)

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“…Produce feature point descriptors. Matching characteristics [14]. The SURF algorithm is stable and keeps the invariance at rotation, scale transformation and brightness.…”

Section: Surf-based Algorithmmentioning

confidence: 99%

Analysis of feature point matching technology in SLAM based on binocular vision

Yu¹

2023

HSET

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With rapidly developing computing technology, intelligent robots have been used extensively in many areas of life, improving work efficiency and lowering labor costs. At the same time, with the increasing number of robot application scenarios, the demand for robot intelligence has also increased. Computer vision-based simultaneous localization and mapping (SLAM) technology is a key technology that helps robots achieve real-time positioning and navigation in order to improve their intelligence. Feature point matching is an important component of this technology. This paper mainly analyzes the current development status of feature point matching technology in visual SLAM. Firstly, a brief introduction was given to the three widely used SLAMs, and the advantages and disadvantages of different SLAM technologies were analyzed. Second, a brief presentation and explanation of the working principle of binocular SLAM technology were provided. Then, the basic principles and key points of feature point recognition and matching techniques in several different algorithms for visual SLAM were summarized. Analyzed the advantages and disadvantages of various algorithms, as well as improvements based on this algorithm. Finally, summarize and provide suggestions for the future development of visual SLAM technology.

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“…Produce feature point descriptors. Matching characteristics [14]. The SURF algorithm is stable and keeps the invariance at rotation, scale transformation and brightness.…”

Section: Surf-based Algorithmmentioning

confidence: 99%

Analysis of feature point matching technology in SLAM based on binocular vision

Yu¹

2023

HSET

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“…Zhou et al ( 2017 ) proposed using the ORB algorithm to extract RGB image features and completing the matching of feature descriptors to improve processing speed and matching accuracy. Srividhya et al ( 2019 ) used speeded-up robust features (SURF), another useful visual processing algorithm, to describe local features. Since the scale-invariant feature transform (SIFT) is highly invariant to scaling and rotation of images, the extracted features are hardly affected by camera tilt (Hu and Liu, 2019 ).…”

Section: Introductionmentioning

confidence: 99%

Robustness Improvement of Visual Templates Matching Based on Frequency-Tuned Model in RatSLAM

Shu

et al. 2020

Front. Neurorobot.

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This paper describes an improved brain-inspired simultaneous localization and mapping (RatSLAM) that extracts visual features from saliency maps using a frequency-tuned (FT) model. In the traditional RatSLAM algorithm, the visual template feature is organized as a one-dimensional vector whose values only depend on pixel intensity; therefore, this feature is susceptible to changes in illumination intensity. In contrast to this approach, which directly generates visual templates from raw RGB images, we propose an FT model that converts RGB images into saliency maps to obtain visual templates. The visual templates extracted from the saliency maps contain more of the feature information contained within the original images. Our experimental results demonstrate that the accuracy of loop closure detection was improved, as measured by the number of loop closures detected by our method compared with the traditional RatSLAM system. We additionally verified that the proposed FT model-based visual templates improve the robustness of familiar visual scene identification by RatSLAM.

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