MLSS-VO: A Multi-Level Scale Stabilizer with Self-Supervised Features for Monocular Visual Odometry in Target Tracking

Wang, Zihao; Yang, Sen; Shi, Mengji; Qin, Kaiyu

doi:10.3390/electronics11020223

Cited by 3 publications

(1 citation statement)

References 25 publications

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“…Three-dimensional positioning algorithm: The SPPM method is adopted to quickly solve the depth value of the object feature point, in order to obtain the spatial coordinates of the object and finally acquire the spatial trajectory information of the object. In the author's previous work [19,20,30,31], we discuss in detail object feature extraction and detection (FDA-SSD), planar feature moving object localization method (SPPM), and an autonomous localization method for motion platforms based on object tracking (MLSS-VO) respectively. In order not to distract the reader, we will not elaborate on what has been published in the manuscript, instead, we will focus on the elaboration of the image augmentation method.…”

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confidence: 99%

An Image Augmentation Method Based on Limited Samples for Object Tracking Based on Mobile Platform

Wang

Yang

Shi

et al. 2022

Sensors

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This paper proposes an image augmentation model of limited samples on the mobile platform for object tracking. The augmentation method mainly aims at the detection failure caused by the small number of effective samples, jitter of tracking platform, and relative rotation between camera and object in the tracking process. Aiming at the object tracking problem, we first propose to use geometric projection transformation, multi-directional overlay blurring, and random background filling to improve the generalization ability of samples. Then, selecting suitable traditional augmentation methods as the supplements, an image augmentation model with an adjustable probability factor is provided to simulate various kinds of samples to help the detection model carry out more reliable training. Finally, combined with a spatial localization algorithm based on geometric constraints proposed by the author’s previous work, a framework for object tracking with an image augmentation method is proposed. SSD, YOLOv3, YOLOv4, and YOLOx are adopted in the experiment of this paper as the detection models. And a large number of object recognition and object tracking experiments are carried out by combining with common data sets OTB50 and OTB100 as well as the OTMP data set proposed by us for mobile platform. The augmented module proposed in this paper is conducive for the detection model to improve the detection accuracy by at least 10%. Especially for objects with planar characteristics, the affine and projection transformation used in this paper can greatly improve the detection accuracy of the model. Based on the object tracking framework of our augmented model, the RMSE is estimated to be less than 4.21 cm in terms of the actual tracking of indoor objects.

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An Image Augmentation Method Based on Limited Samples for Object Tracking Based on Mobile Platform

Wang

Yang

Shi

et al. 2022

Sensors

Self Cite

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Robust Visual SLAM in Dynamic Environment Based on Motion Detection and Segmentation

Yu,

Shen,

et al. 2024

Journal of Autonomous Vehicles and Systems

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In this study, we propose a robust and accurate SLAM method for dynamic environments. Our approach combines sparse optical flow with epipolar geometric constraints to detect motion, determining whether a priori dynamic objects are moving. By integrating semantic segmentation with this motion detection, we can effectively remove dynamic keypoints, eliminating the influence of dynamic objects. This dynamic object removal technique is integrated into ORB-SLAM2, en-hancing its robustness and accuracy for localization and mapping. Experimental results on the TUM dataset demonstrate that our proposed system significantly reduces pose estimation error compared to ORB-SLAM2. Specifically, the RMSE and standard deviation (S.D.) of ORB-SLAM2 are reduced by up to 97.78% and 97.91%, respectively, in highly dynamic se-quences, markedly improving robustness in dynamic environments. Furthermore, compared to other similar SLAM methods, our method reduces RMSE and S.D. by up to 69.26% and 73.03%, respectively. Dense semantic maps generated by our method also closely align with the ground truth.

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Robust Visual SLAM in Dynamic Environment Based on Moving Detection and Segmentation

Yu,

Shen,

et al. 2023

Preprint

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In this study, a robust and accurate SLAM method for dynamic environments is proposed. Sparse optical flow and epipolar geometric constraint are combined to conduct moving detection by judging whether a priori dynamic object is in motion. Semantic segmentation is combined with moving detection to perform dynamic keypoints removal by removing dynamic objects. The dynamic objects removal method is integrated into ORB-SLAM2, enabling robust, accurate localization and mapping. Experiments on TUM datasets show that compared with ORB-SLAM2, the proposed system can significantly reduce the pose estimation error, and the RMSE and S.D. of ORB-SLAM2 are reduced by up to 97.78% and 97.91% respectively under high dynamic sequences, improving the robustness in dynamic environments. Compared with other similar SLAM methods, the RMSE and S.D. of the proposed method are reduced by up to 69.26% and 73.03% respectively. Dense semantic maps built with our method are also much closer to the groundtruth.

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MLSS-VO: A Multi-Level Scale Stabilizer with Self-Supervised Features for Monocular Visual Odometry in Target Tracking

Cited by 3 publications

References 25 publications

An Image Augmentation Method Based on Limited Samples for Object Tracking Based on Mobile Platform

An Image Augmentation Method Based on Limited Samples for Object Tracking Based on Mobile Platform

Robust Visual SLAM in Dynamic Environment Based on Motion Detection and Segmentation

Robust Visual SLAM in Dynamic Environment Based on Moving Detection and Segmentation

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