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A Comprehensive Study of Systems Challenges in visual simultaneous localization and mapping Systems

ACM TRANSACTIONS ON EMBEDDED COMPUTING SYSTEMS 2025年第1期24卷 1-31页

作者： Semenova, Sofiya Ko, Steven Liu, Yu David Ziarek, Lukasz Dantu, Karthik Univ Buffalo Comp Sci & Engn Buffalo NY 14222 USA Simon Fraser Univ Comp Sci Burnaby BC Canada SUNY Binghamton Comp Sci Binghamton NY USA

visual SLAM systems are concurrent, performance-critical systems that respond to real-time environmental conditions and are frequently deployed on resource-constrained hardware. Previous work has identified three interconnected systems challenges to building consistent, accurate, and robust SLAM systems-timeliness, concurrency, and context awareness. In this article, we analyze three popular, state-of-the-art frameworks with varying system designs and optimization techniques, and we quantify the extent to which they are affected by the aforementioned system challenges. We find that all SLAM systems must balance the interconnected nature of timeliness and accuracy, and different system designs and optimization techniques uniquely address this tension. Global-map-based SLAM systems typically achieve the best performance but suffer in resource-constrained scenarios with increased concurrency. Across all SLAM systems, incorporating context awareness into decision-making would mitigate the impact of timeliness and concurrency on accuracy in resource-constrained scenarios.

关键词： visual simultaneous localization and mapping mobile systems

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Edge-SLAM: Edge-Assisted visual simultaneous localization and mapping

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ACM TRANSACTIONS ON EMBEDDED COMPUTING SYSTEMS 2023年第1期22卷 1-31页

作者： Ben Ali, Ali J. Kouroshli, Marziye Semenova, Sofiya Hashemifar, Zakieh Sadat Ko, Steven Y. Dantu, Karthik SUNY Binghamton 4400 Vestal Pkwy East Binghamton NY 13902 USA Univ Buffalo 106 Davis Hall New York NY 14260 USA Zoox 1149 Chess Dr Foster City CA 94404 USA Simon Fraser Univ 8888 Univ Dr Burnaby BC V5A 1S6 Canada

localization in urban environments is becoming increasingly important and used in tools such asARCore [18], ARKit [34] and others. One popular mechanism to achieve accurate indoor localization and a map of the space is using visual simultaneous localization and mapping (visual-SLAM). However, visual-SLAM is known to be resource-intensive in memory and processing time. Furthermore, some of the operations grow in complexity over time, making it challenging to run on mobile devices continuously. Edge computing provides additional compute and memory resources to mobile devices to allow offloading tasks without the large latencies seen when offloading to the cloud. In this article, we present Edge-SLAM, a system that uses edge computing resources to offload parts of visual-SLAM. We use ORB-SLAM2 [50] as a prototypical visual-SLAM system and modify it to a split architecture between the edge and the mobile device. We keep the tracking computation on the mobile device and move the rest of the computation, i.e., local mapping and loop closing, to the edge. We describe the design choices in this effort and implement them in our prototype. Our results show that our split architecture can allow the functioning of the visual-SLAM system long-term with limited resources without affecting the accuracy of operation. It also keeps the computation and memory cost on the mobile device constant, which would allow for the deployment of other end applications that use visual-SLAM. We perform a detailed performance and resources use (CPU, memory, network, and power) analysis to fully understand the effect of our proposed split architecture.

关键词： visual simultaneous localization and mapping edge computing split architecture mobile systems localization mapping concurrency

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A Comparative Review on Enhancing visual simultaneous localization and mapping with Deep Semantic Segmentation

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SENSORS 2024年第11期24卷 3388页

作者： Liu, Xiwen He, Yong Li, Jue Yan, Rui Li, Xiaoyu Huang, Hui Minist Nat Resources Key Lab Urban Land Resources Monitoring & Simulat Shenzhen 518034 Peoples R China Chongqing Jiaotong Univ Sch Smart City Chongqing 400074 Peoples R China Chongqing Jiaotong Univ Coll Traff & Transportat Chongqing 400074 Peoples R China Chongqing Digital City Technol Co Ltd Chongqing 400074 Peoples R China

visual simultaneous localization and mapping (VSLAM) enhances the navigation of autonomous agents in unfamiliar environments by progressively constructing maps and estimating poses. However, conventional VSLAM pipelines often exhibited degraded performance in dynamic environments featuring mobile objects. Recent research in deep learning led to notable progress in semantic segmentation, which involves assigning semantic labels to image pixels. The integration of semantic segmentation into VSLAM can effectively differentiate between static and dynamic elements in intricate scenes. This paper provided a comprehensive comparative review on leveraging semantic segmentation to improve major components of VSLAM, including visual odometry, loop closure detection, and environmental mapping. Key principles and methods for both traditional VSLAM and deep semantic segmentation were introduced. This paper presented an overview and comparative analysis of the technical implementations of semantic integration across various modules of the VSLAM pipeline. Furthermore, it examined the features and potential use cases associated with the fusion of VSLAM and semantics. It was found that the existing VSLAM model continued to face challenges related to computational complexity. Promising future research directions were identified, including efficient model design, multimodal fusion, online adaptation, dynamic scene reconstruction, and end-to-end joint optimization. This review shed light on the emerging paradigm of semantic VSLAM and how deep learning-enabled semantic reasoning could unlock new capabilities for autonomous intelligent systems to operate reliably in the real world.

关键词： semantic segmentation visual simultaneous localization and mapping deep learning dynamic environments comparative review

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Accurate visual simultaneous localization and mapping (SLAM) against Around View Monitor (AVM) Distortion Error Using Weighted Generalized Iterative Closest Point (GICP)

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SENSORS 2023年第18期23卷 7947-7947页

作者： Lee, Yangwoo Kim, Minsoo Ahn, Joonwoo Park, Jaeheung Seoul Natl Univ Grad Sch Convergence Sci & Technol Dynam Robot Syst DYROS Lab Seoul 08826 South Korea Samsung Adv Inst Technol Samsung Elect Suwon 16678 South Korea Seoul Natl Univ Automat & Syst Res Inst ASRI Res Inst Convergence Sci R Seoul 08826 South Korea Adv Inst Convergence Technol Suwon 16229 South Korea

Accurately estimating the pose of a vehicle is important for autonomous parking. The study of around view monitor (AVM)-based visual simultaneous localization and mapping (SLAM) has gained attention due to its affordability, commercial availability, and suitability for parking scenarios characterized by rapid rotations and back-and-forth movements of the vehicle. In real-world environments, however, the performance of AVM-based visual SLAM is degraded by AVM distortion errors resulting from an inaccurate camera calibration. Therefore, this paper presents an AVM-based visual SLAM for autonomous parking which is robust against AVM distortion errors. A deep learning network is employed to assign weights to parking line features based on the extent of the AVM distortion error. To obtain training data while minimizing human effort, three-dimensional (3D) Light Detection and Ranging (LiDAR) data and official parking lot guidelines are utilized. The output of the trained network model is incorporated into weighted Generalized Iterative Closest Point (GICP) for vehicle localization under distortion error conditions. The experimental results demonstrate that the proposed method reduces localization errors by an average of 39% compared with previous AVM-based visual SLAM approaches.

关键词： visual simultaneous localization and mapping autonomous parking AVM distortion error deep learning weighted Generalized Iterative Closest Point

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Real-Time Artificial Intelligence Based visual simultaneous localization and mapping in Dynamic Environments - a Review

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JOURNAL OF INTELLIGENT & ROBOTIC SYSTEMS 2022年第1期105卷 15-15页

作者： Aasim, Wan Faris Aizat Wan Okasha, Mohamed Faris, Waleed Fekry United Arab Emirates Univ UAEU Coll Engn Mech & Aerosp Engn Dept Al Ain U Arab Emirates Int Islamic Univ Malaysia IIUM Coll Engn Mech & Aerosp Engn Dept Kuala Lumpur Malaysia

This paper aims to review current approaches for real-time artificial intelligence-based visual simultaneous localization and mapping (vSLAM) operating in dynamic environments. Through the review of several relatively recent published papers for vSLAM in dynamic environments, an attempt is made to explain the concept of simultaneous localization and mapping (SLAM) and its purpose;identify the general framework for real-time AI-based vSLAM approaches in dynamic environments and highlighting the potential solutions that has been developed and their significant results. All related information regarding this topic was obtained with the intention to answer three main questions. Firstly, how do robots localize and map within an unknown environment? Secondly, how can state-of-the-art vSLAM be modified with an AI algorithm to function within a dynamic environment? And lastly, what level of success has these approaches achieved in developing methods for real-time AI-based vSLAM in dynamic environments? The paper intends to provide readers with a clearer general understanding of SLAM and acts as a road map for steps to move forward in developing viable approaches for real-time vSLAM in dynamic environments based on artificial intelligence.

关键词： visual simultaneous localization and mapping vSLAM Dynamic environments Artificial intelligence Real-time AI Online Machine learning Neural networks

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Eliminating short-term dynamic elements for robust visual simultaneous localization and mapping using a coarse-to-fine strategy

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JOURNAL OF ELECTRONIC IMAGING 2022年第5期31卷 053018-053018页

作者： Fu, Fuji Yang, Jinfu Zhang, Jiahui Ma, Jiaqi Beijing Univ Technol Fac Informat Technol Beijing Peoples R China Beijing Key Lab Computat Intelligence & Intellige Beijing Peoples R China

visual simultaneous localization and mapping (VSLAM) is one of the foremost principal technologies for intelligent robots to implement environment perception. Many research works have focused on proposing comprehensive and integrated systems based on the static environment assumption. However, the elements whose motion status changes frequently, namely short-term dynamic elements, can significantly affect the system performance. Therefore, it is extremely momentous to cope with short-term dynamic elements to make the VSLAM system more adaptable to dynamic scenes. This paper proposes a coarse-to-fine elimination strategy for short-term dynamic elements based on motion status check (MSC) and feature points update (FPU). First, an object detection module is designed to obtain semantic information and screen out the potential short-term dynamic elements. And then an MSC module is proposed to judge the true status of these elements and thus ultimately determine whether to eliminate them. In addition, an FPU module is introduced to update the extracted feature points according to calculating the dynamic region factor to improve the robustness of VSLAM system. Quantitative and qualitative experiments on two challenging public datasets are performed. The results demonstrate that our method effectively eliminates the influence of short-term dynamic elements and outperforms other state-of-the-art methods. (c) 2022 SPIE and IS&T

关键词： visual simultaneous localization and mapping short-term dynamic elements motion status check feature points update coarse-to-fine

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Indirect visual simultaneous localization and mapping Based on Linear Models

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IEEE SENSORS JOURNAL 2020年第5期20卷 2738-2747页

作者： Chien, Chiang-Heng Hsu, Chen-Chien James Wang, Wei-Yen Chiang, Hsin-Han Natl Taiwan Normal Univ Elect Engn Taipei 106 Taiwan

This paper proposes an indirect visual simultaneous localization and mapping (V-SLAM) for large-scale outdoor environments based on linear models. With the scale-invariant feature transform (SIFT) algorithm, a design combining key-frame insertion and map management is proposed to avoid redundant computation and unreliable landmarks. Then, an iterative linear equation is employed to update landmarks. With the supplement of reliable map data, absolute camera poses can be obtained using a simple linear equation based on a linearity index. In addition, a linear model helps the system to detect candidates of looped frames and discard invalid loops by means of an outlier weight function. If a loop is detected, an improved trajectory bending algorithm is introduced to optimize the estimations of poses and landmarks. To evaluate the effectiveness of the proposed approach, extensive experiments are conducted using various sequences from a well-known public dataset. Experimental results show that the proposed V-SLAM outperforms other existing methods in terms of various metrics.

关键词： visual simultaneous localization and mapping indirect method localization map building loop closure

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Advancements and Challenges in visual-Only simultaneous localization and mapping (V-SLAM): A Systematic Review 2

Advancements and Challenges in Visual-Only Simultaneous Loca...

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2nd International Conference on Sustainable Computing and Smart Systems (ICSCSS)

作者： Singh, Balveer Kumar, Puneet Kaur, Narinder Chandigarh Univ Comp Sci & Engn Gharuan India

ISBN: (纸本)9798350391558;9798350379990

visual simultaneous localization and mapping (V-SLAM) is a technique used by autonomous navigation systems that leverages visual sensors, such as cameras, to create a map of an unknown environment while simultaneously tracking the sensor's location. While most reviews focus on SLAM technology incorporating multiple sensors, this paper comprehensively reviews the historical evolution and recent advancements in visual-only SLAM algorithms. The main objectives are to address challenges faced by traditional SLAM methods and highlight the need for a visual-only approach. This research study has conducted a systematic literature review, highlighting the integration of deep learning for enabling enhanced feature extraction and semantic understanding. The research findings show significant progress from early feature-based methods like Mono-SLAM to advanced algorithms like ORB-SLAM3, incorporating robust loop closure and global *** proposed model is then evaluated using benchmark datasets like KITTI and TUM RGB-D, further these advancements demonstrate improvements in accuracy and efficiency. This review offers unique insights and future research directions, benefiting researchers and practitioners.

关键词： visual simultaneous localization and mapping Autonomous Navigation Robotics Robustness

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Comparison of visual simultaneous localization and mapping Methods for Fixed-Wing Aircraft Using SLAMBench2

Comparison of Visual Simultaneous Localization and Mapping M...

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作者： Patrick R. Latcham Air Force Institute of Technology

学位级别：硕士

visual simultaneous localization and mapping (VSLAM) algorithms have evolved rapidly in the last few years, however there has been little research evaluating current algorithm's effectiveness and limitations when applied to tracking the position of a fixed-wing aerial vehicle. This research looks to evaluate current monocular VSLAM algorithms' performance on aerial vehicle datasets using the SLAMBench2 benchmarking suite. The algorithms tested are MonoSLAM, PTAM, OKVIS, LSDSLAM, ORB-SLAM2, and SVO, all of which are built into the SLAMBench2 software. The algorithms' performance is evaluated using simulated datasets generated in the AftrBurner Engine. The datasets were designed to test the quality of each algorithm's tracking solution, as well as finding any dependence on camera field of view (FOV), aircraft altitude, bank angle, and bank rate. Through these tests, it was found that LSDSLAM, ORB-SLAM2, and SVO are good candidates for further research, with MonoSLAM, PTAM, and OKVIS failing to track any datasets. All algorithms were found to fail when the capturing camera had a horizontal FOV of less than 60 degrees, with peak performance occurring at a FOV of 75 degrees or above. LSDSLAM was found to fail when the aircraft bank angle exceeded half of the camera's FOV, and SVO was found to fail below 450 meters altitude. The simulations were also tested against a comparable real world dataset, with agreeable results, although the FOV of the real world dataset was too small to be a particularly useful test. Further research is required to determine the applicability of these results to the real world, as well as fuse VSLAM algorithms with other sensors and solutions to form a more robust navigation solution.

关键词： visual simultaneous localization and mapping visual Odometry

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Gyroscope Assisted Scalable visual simultaneous localization and mapping

Gyroscope Assisted Scalable Visual Simultaneous Localization...

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Ubiquitous Positioning Indoor Navigation and Location Based Service (UPINLBS)

作者： Babu, Benzun P. Wisely Cyganski, David Duckworth, James Worcester Polytech Inst Worcester MA 01609 USA

ISBN: (纸本)9781479960040

This paper describes the development and evaluation of an indoor localization algorithm using visual simultaneous localization and mapping (VSLAM) aided by gyroscope sensor information. Indoor environments pose several challenges which could cause a vision only system to fail due to tracking errors. Investigation revealed significant feature loss in a vision only system when traversing plain walls, windows and staircases. However, the addition of a gyroscope helps in handling such difficult conditions by providing additional rotational information. A portable system consisting of an Inertial Measurement Unit (IMU) and a stereo camera has been developed for indoor mapping. The images and gyroscope rates acquired by the system are stored and post-processed using a new Gyroscope Assisted Scalable visual simultaneous localization and mapping Algorithm (GA-ScaViSLAM). The algorithm has been evaluated for data-sets collected in the Atwater Kent building, Worcester Polytechnic Institute. This algorithm was found to be more robust in comparison to the vision only system. The Ga-ScaViSLAM was found to have an error (rms) of 0.6 m in the indoor environment over a total path length of 77m.

关键词： visual simultaneous localization and mapping Indoor SLAM Gyroscope Stereo-camera Sensor fusion

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