MSIFT+: A Mahalanobis Distance- and BBF-Based Feature Matching Framework for Vision-Guided Robotic Grasping

Wang, Zhen; Ma, Yao; Yong, Zheng; Zhou, Huaijuan; Liu, Ming; Li, Zhiqing

doi:10.3390/app16105120

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MSIFT+: A Mahalanobis Distance- and BBF-Based Feature Matching Framework for Vision-Guided Robotic Grasping

by

Zhen Wang

¹,

Yao Ma

¹,

Zheng Yong

¹,

Huaijuan Zhou

²

,

Ming Liu

^2,*

and

Zhiqing Li

^1,*

¹

College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China

²

School of Interdisciplinary Science, Beijing Institute of Technology, Beijing 100081, China

^*

Authors to whom correspondence should be addressed.

Appl. Sci. 2026, 16(10), 5120; https://doi.org/10.3390/app16105120

Submission received: 13 April 2026 / Revised: 17 May 2026 / Accepted: 19 May 2026 / Published: 20 May 2026

(This article belongs to the Special Issue Advances in Biorobotics and Bionic Systems)

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Abstract

Indoor service robots often face challenges in target localization and robotic grasping under cluttered backgrounds, partial occlusion, and viewpoint variations. To address these issues, this study proposes a vision-guided robotic grasping framework based on an improved feature matching algorithm termed Mahalanobis-accelerated Scale-Invariant Feature Transform Plus (MSIFT+). The proposed method integrates Mahalanobis distance metric reconstruction with a dynamic Best-Bin-First (BBF) search strategy to improve matching robustness and computational efficiency. A multi-scenario indoor dataset was constructed to evaluate the proposed method under rotational variation, weak-texture, and partial occlusion conditions. The results demonstrate that the MSIFT+ algorithm significantly outperforms other methods in cross-scenario consistency and adaptability to weakly textured targets. Furthermore, a binocular vision-guided robotic grasping system was developed and validated through practical robotic experiments. Experimental results confirm that the MSIFT+ algorithm enhances detection performance for small and clustered targets in complex environments. The proposed framework provides an effective and reliable solution for robotic object localization and grasping in complex indoor environments.

Keywords: vision-guided robotic grasping; service robot; robotic manipulation; feature matching optimization

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MDPI and ACS Style

Wang, Z.; Ma, Y.; Yong, Z.; Zhou, H.; Liu, M.; Li, Z. MSIFT+: A Mahalanobis Distance- and BBF-Based Feature Matching Framework for Vision-Guided Robotic Grasping. Appl. Sci. 2026, 16, 5120. https://doi.org/10.3390/app16105120

AMA Style

Wang Z, Ma Y, Yong Z, Zhou H, Liu M, Li Z. MSIFT+: A Mahalanobis Distance- and BBF-Based Feature Matching Framework for Vision-Guided Robotic Grasping. Applied Sciences. 2026; 16(10):5120. https://doi.org/10.3390/app16105120

Chicago/Turabian Style

Wang, Zhen, Yao Ma, Zheng Yong, Huaijuan Zhou, Ming Liu, and Zhiqing Li. 2026. "MSIFT+: A Mahalanobis Distance- and BBF-Based Feature Matching Framework for Vision-Guided Robotic Grasping" Applied Sciences 16, no. 10: 5120. https://doi.org/10.3390/app16105120

APA Style

Wang, Z., Ma, Y., Yong, Z., Zhou, H., Liu, M., & Li, Z. (2026). MSIFT+: A Mahalanobis Distance- and BBF-Based Feature Matching Framework for Vision-Guided Robotic Grasping. Applied Sciences, 16(10), 5120. https://doi.org/10.3390/app16105120

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MSIFT+: A Mahalanobis Distance- and BBF-Based Feature Matching Framework for Vision-Guided Robotic Grasping

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