An RGB-D SLAM algorithm based on adaptive semantic segmentation in dynamic environment

Song, Wei; Li, Zhang

doi:10.1007/s11554-023-01343-2

Cited by 7 publications

(1 citation statement)

References 24 publications

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“…A study of an RGB-D SLAM system for indoor dynamic environments used adaptive semantic segmentation tracking to improve localization accuracy and real-time performance, achieving a 90.57% accuracy increase over ORB-SLAM2 and creating a 3D semantic map for enhanced robot navigation [10].…”

Section: Related Workmentioning

confidence: 99%

A New Method for Classifying Scenes for Simultaneous Localization and Mapping Using the Boundary Object Function Descriptor on RGB-D Points

Lomas-Barrie,

Suarez-Espinoza,

Hernandez-Chavez

et al. 2023

Sensors

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Scene classification in autonomous navigation is a highly complex task due to variations, such as light conditions and dynamic objects, in the inspected scenes; it is also a challenge for small-factor computers to run modern and highly demanding algorithms. In this contribution, we introduce a novel method for classifying scenes in simultaneous localization and mapping (SLAM) using the boundary object function (BOF) descriptor on RGB-D points. Our method aims to reduce complexity with almost no performance cost. All the BOF-based descriptors from each object in a scene are combined to define the scene class. Instead of traditional image classification methods such as ORB or SIFT, we use the BOF descriptor to classify scenes. Through an RGB-D camera, we capture points and adjust them onto layers than are perpendicular to the camera plane. From each plane, we extract the boundaries of objects such as furniture, ceilings, walls, or doors. The extracted features compose a bag of visual words classified by a support vector machine. The proposed method achieves almost the same accuracy in scene classification as a SIFT-based algorithm and is 2.38× faster. The experimental results demonstrate the effectiveness of the proposed method in terms of accuracy and robustness for the 7-Scenes and SUNRGBD datasets.

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Section: Related Workmentioning

confidence: 99%