A survey: which features are required for dynamic visual simultaneous localization and mapping?

Xu, Zhuoqun; Zheng, Rong; Wu, Yihong

doi:10.1186/s42492-021-00086-w

Cited by 19 publications

(10 citation statements)

References 90 publications

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“…Among them, SLAM based on feature point method emerged a number of excellent SLAM systems for dynamic environment processing. According to the amount of feature information used for matching, visual features can be divided into two levels: low-level features such as pixel patches, points, or lines, and high-level features such as semantically labeled objects [26]. Low-level features focus on local details such as textures or the geometric primitives of objects and scenes.…”

Section: Related Workmentioning

confidence: 99%

Real-Time SLAM Based on Dynamic Feature Point Elimination in Dynamic Environment

Gao,

Li,

et al. 2023

IEEE Access

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Slam (simultaneous localization and mapping) play an important role in the field of artificial and driverless intelligence. A real-time dynamic visual SLAM algorithm based on an object detection network is proposed to address the robustness and camera localization accuracy issues caused by dynamic objects in indoor dynamic scenes. The YOLOv5s model, which has the smallest depth and feature map width in the YOLOv5 series, is chosen as the object detection network. The backbone network is replaced with the lightweight ShuffleNetv2 network. Experimental results on the VOC2007 dataset show that the YOLOv5-LITE model reduces the network parameters by 41.89%and speeds up the runtime by 39.00% compared to the YOLOv5s model. A motion level division strategy is adopted to provide prior information to the object detection network. In the tracking thread of the visual SLAM system, a parallel thread combining the improved object detection network and multi-view geometry is introduced to eliminate dynamic feature points. The experimental results demonstrate that in dynamic scenes, the proposed algorithm improves the camera localization accuracy by an average of 85.38% compared to ORB-SLAM2. Finally, experiments in a real environment are conducted to validate the effectiveness of the algorithm.

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Section: Related Workmentioning

confidence: 99%

Real-Time SLAM Based on Dynamic Feature Point Elimination in Dynamic Environment

Gao,

Li,

et al. 2023

IEEE Access

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“…Precise positioning is difficult. Precision under uncertainty and is error aware [64]. Low accuracy photogrammetry.…”

Section: Multi-uav Trajectory Planning Servicesmentioning

confidence: 99%

Simultaneous Localization and Mapping (SLAM) and Data Fusion in Unmanned Aerial Vehicles: Recent Advances and Challenges

Gupta

Fernando

2022

Drones

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This article presents a survey of simultaneous localization and mapping (SLAM) and data fusion techniques for object detection and environmental scene perception in unmanned aerial vehicles (UAVs). We critically evaluate some current SLAM implementations in robotics and autonomous vehicles and their applicability and scalability to UAVs. SLAM is envisioned as a potential technique for object detection and scene perception to enable UAV navigation through continuous state estimation. In this article, we bridge the gap between SLAM and data fusion in UAVs while also comprehensively surveying related object detection techniques such as visual odometry and aerial photogrammetry. We begin with an introduction to applications where UAV localization is necessary, followed by an analysis of multimodal sensor data fusion to fuse the information gathered from different sensors mounted on UAVs. We then discuss SLAM techniques such as Kalman filters and extended Kalman filters to address scene perception, mapping, and localization in UAVs. The findings are summarized to correlate prevalent and futuristic SLAM and data fusion for UAV navigation, and some avenues for further research are discussed.

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“…Soares et al [ 19 ] and Han et al [ 20 ] retained static priori dynamic objects although they combined optical flow to determine dynamic objects, which will reduce localization accuracy. Typical methods for point feature matching can be summarized as descriptor-based and pixel-based methods [ 21 ], due to the difficulty of establishing sufficient dynamic data associations, the descriptor-based method can be challenging to apply to highly dynamic objects [ 22 ]. As such, many systems resort to using the optical flow method to address this issue [ 23 , 24 , 25 ], outliers are then removed from the essential matrix using the RANSAC method [ 26 ].…”

Section: Introductionmentioning

confidence: 99%

An Adaptive ORB-SLAM3 System for Outdoor Dynamic Environments

Zang

Zhang

2023

Sensors

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Recent developments in robotics have heightened the need for visual SLAM. Dynamic objects are a major problem in visual SLAM which reduces the accuracy of localization due to the wrong epipolar geometry. This study set out to find a new method to address the low accuracy of visual SLAM in outdoor dynamic environments. We propose an adaptive feature point selection system for outdoor dynamic environments. Initially, we utilize YOLOv5s with the attention mechanism to obtain a priori dynamic objects in the scene. Then, feature points are selected using an adaptive feature point selector based on the number of a priori dynamic objects and the percentage of a priori dynamic objects occupied in the frame. Finally, dynamic regions are determined using a geometric method based on Lucas-Kanade optical flow and the RANSAC algorithm. We evaluate the accuracy of our system using the KITTI dataset, comparing it to various dynamic feature point selection strategies and DynaSLAM. Experiments show that our proposed system demonstrates a reduction in both absolute trajectory error and relative trajectory error, with a maximum reduction of 39% and 30%, respectively, compared to other systems.

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A survey: which features are required for dynamic visual simultaneous localization and mapping?

Cited by 19 publications

References 90 publications

Real-Time SLAM Based on Dynamic Feature Point Elimination in Dynamic Environment

Real-Time SLAM Based on Dynamic Feature Point Elimination in Dynamic Environment

Simultaneous Localization and Mapping (SLAM) and Data Fusion in Unmanned Aerial Vehicles: Recent Advances and Challenges

An Adaptive ORB-SLAM3 System for Outdoor Dynamic Environments

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