Modified Keyframe Selection Algorithm and Map Visualization Based on ORB-SLAM2

Xie, Pengfa; Su, Weihua; Li, Boyang; Jian, Rui; Huang, Ruqiang; Zhang, Shiyue; Wei, Jiacheng

doi:10.1109/icras49812.2020.9135058

Cited by 7 publications

(5 citation statements)

References 14 publications

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“…In general, the data association is constructed by means of feature matching or feature tracking; however, the criteria used for keyframe selection are distinctive. The majority used the number of features matched/tracked or the inlier ratio between camera observation and previous frames, or the existing map, for view change thresholding [45][46][47][48][49][50][51]. Other methods used feature correspondence to construct auxiliary graph structures to determine the degree of overlap between the camera observation and the previously detected keyframes [21,52,53].…”

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Keyframe Selection for Visual Localization and Mapping Tasks: A Systematic Literature Review

2023

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Visual localization and mapping algorithms attempt to estimate, from images, geometrical models that explain ego motion and the positions of objects in a real scene. The success of these tasks depends directly on the quality and availability of visual data, since the information is recovered from visual changes in images. Keyframe selection is a commonly used approach to reduce the amount of data to be processed as well as to prevent useless or wrong information to be considered during the optimization. This study aims to identify, analyze, and summarize the methods present in the literature for keyframe selection within the context of visual localization and mapping. We adopt a systematic literature review (SLR) as the basis of our work, built on top of a well-defined methodology. To the best of our knowledge, this is the first review related to this topic. The results show that there is a lack of studies present in the literature that directly address the keyframe selection problem in this application context and a deficiency in the testing and validation of the proposed methods. In addition to these findings, we also propose an updated categorization of the proposed methods on top of the well-discussed categories present in the literature. We believe that this SLR is a step toward developing a body of knowledge in keyframe selection within the context of visual localization and mapping tasks by encouraging the development of more theoretical and less heuristic methods and a systematic testing and validation process.

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Section: Resultsmentioning

confidence: 99%

“…ACM Digital Library [30,36,38,45,47,60] IEEE Digital Library [6,13,20,22,23,[31][32][33]44,46,[48][49][50][51][52]54,56,57,[65][66][67][73][74][75][76][77] ScienceDirect [21,35,40,41,43,53,58,62,[78][79][80] Springer Link [8,29,34,37,39,42,55,59,63,…”

Section: Appendix B Selected Studiesmentioning

confidence: 99%

Keyframe Selection for Visual Localization and Mapping Tasks: A Systematic Literature Review

2023

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“…The inter-frame relative motion quantity D is calculated from R and t obtained from the above derivation and the threshold Q is set in the system as shown in Eq. (6).…”

Section: Improved Orb-slam2 Keyframe Selection Algorithmmentioning

confidence: 99%

“…Meanwhile, Simultaneous Localization and Mapping (SLAM) technology is the core of mobile robots to realize autonomous path planning [1][2][3][4] [5]. SLAM technology is divided into laser SLAM and vision SLAM according to the specific type of sensor equipped [6] [7][8] [9], the radar sensor with higher accuracy is expensive and has higher power consumption, while the SLAM scheme equipped with a camera as a sensor has the advantages of low equipment cost and small size compared to it, and it can provide the visual image recognition information with better robustness, so the vision SLAM has gradually become the mainstream scheme for researchers with a higher cost-effectiveness [10] [11] [12].…”

Section: Introductionmentioning

confidence: 99%

Research on map construction based on ORB-SLAM2 algorithm with improved keyframe selection

Zhou,

Li,

Yao

et al. 2023

Preprint

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Aiming at the problems that the traditional ORB-SLAM2 algorithm has too little keyframe information in dynamic scenes, which leads to the loss of tracking and localization, the error generated by the camera trajectory, and the constructed sparse point cloud maps can't be directly applied to navigation and path planning, an ORB-SLAM2 algorithm based on the improvement of keyframe selection is proposed. Firstly, on the basis of the original ORB-SLAM2 framework, the key frames are screened by adding the corner factor and translation transition factor of the relative motion amount between frames in the neighboring image frames, and the new feature point tracking is added to improve the accuracy of the key frame selection. Then the dense map building and OctoMap threads are added to the three threads of the original system, and the ORB-SLAM2 algorithm with improved keyframe selection is used to build a dense point cloud map and converted and compressed into OctoMaps, and finally the effectiveness of the algorithm is verified on the TUM dataset. The experimental results show that the ORB-SLAM2 algorithm with improved keyframe selection can effectively select keyframe information, the tracking and localization accuracy is significantly improved under the premise of guaranteeing the real-time performance of the system, and the maximum absolute trajectory error is reduced by 49.1% on average in different datasets, and the constructed OctoMap can be stored in a much lower amount of memory and can be used directly for the navigation planning of the robot.

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“…For example, Qin et al employ this method to select keyframes in VINS-MONO [10]; a similar approach is also applied by Harmat et al [11]. Xie [12] further proposes a repetitive motion detection method to reduce redundant keyframes due to the back-and-forth movement of the camera.…”

Section: Keyframe Selectionmentioning

confidence: 99%

InfoLa-SLAM: Efficient Lidar-Based Lightweight Simultaneous Localization and Mapping with Information-Based Keyframe Selection and Landmarks Assisted Relocalization

Lin,

Dong,

et al. 2023

Remote Sensing

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This work reports an information-based landmarks assisted simultaneous localization and mapping (InfoLa-SLAM) in large-scale scenes using single-line lidar. The solution employed two novel designs. The first design was a keyframe selection method based on Fisher information, which reduced the computational cost of the nonlinear optimization for the back-end of SLAM by selecting a relatively small number of keyframes while ensuring the accuracy of mapping. The Fisher information was acquired from the point cloud registration between the current frame and the previous keyframe. The second design was an efficient global descriptor for place recognition, which was achieved by designing a unique graphical feature ID to effectively match the local map with the global one. The results showed that compared with traditional keyframe selection strategies (e.g., based on time, angle, or distance), the proposed method allowed for a 35.16% reduction in the number of keyframes in a warehouse with an area of about 10,000 m2. The relocalization module demonstrated a high probability (96%) of correction even under high levels of measurement noise (0.05 m), while the time consumption for relocalization was below 28 ms. The proposed InfoLa-SLAM was also compared with Cartographer under the same dataset. The results showed that InfoLa-SLAM achieved very similar mapping accuracy to Cartographer but excelled in lightweight performance, achieving a 9.11% reduction in the CPU load and a significant 56.67% decrease in the memory consumption.

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Modified Keyframe Selection Algorithm and Map Visualization Based on ORB-SLAM2

Cited by 7 publications

References 14 publications

Keyframe Selection for Visual Localization and Mapping Tasks: A Systematic Literature Review

Keyframe Selection for Visual Localization and Mapping Tasks: A Systematic Literature Review

Research on map construction based on ORB-SLAM2 algorithm with improved keyframe selection

InfoLa-SLAM: Efficient Lidar-Based Lightweight Simultaneous Localization and Mapping with Information-Based Keyframe Selection and Landmarks Assisted Relocalization

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