Evaluating the Advantages of Remote SLAM on an Edge Cloud

Sossalla, Peter; Rischke, Justus; Hofer, Johannes; Fitzek, Frank H. P.

doi:10.1109/etfa45728.2021.9613415

Cited by 12 publications

(4 citation statements)

References 13 publications

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“…With the upcoming usage of private 5G networks [22], virtualized robot control [23] with edge computing has gained traction. The study [24] demonstrated that the increased computing power supplied by edge computing speeds up the SLAM processing and reduces the load on the mobile end device. Recent studies [25]- [27] have demonstrated that components of the SLAM method can be reliably offloaded to the network.…”

Section: A Motivation For Slam Network Offloadingmentioning

confidence: 97%

“…The processing of SLAM is computationally and memory intensive [24], especially LM and LC. To further illustrate the influence of the computing power on the SLAM processing, we plot the average measured processing times of the ORB-SLAM2 modules as a function of the number of available CPU cores in a Virtual Machine (VM) in Figure 2.…”

Section: B Offloading Slam Computingmentioning

confidence: 99%

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DynNetSLAM: Dynamic Visual SLAM Network Offloading

et al. 2022

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Existing Visual Simultaneous Localization And Mapping (vSLAM) approaches that offload the complex self-localization computations from mobile robots over a wireless network to edge computing are limited to static offloading, i.e., the offloaded computation tasks are offloaded permanently. However, wireless networks are inherently dynamic and may excessively delay the transmissions between a mobile device and the edge during periods of poor wireless network quality, e.g., from fading or temporary obstructions. We propose and evaluate Dynamic Visual SLAM Network Offloading (DynNetSLAM) to dynamically adapt the vSLAM computation offloading according to the measured wireless network latency. As groundwork towards developing DynNetSLAM, we first enhance the existing state-of-the-art vSLAM approaches through judicious parameter settings and parallel map updates to enable the tracking of common fast vSLAM data sets. We introduce an offloading latency threshold along with a safe zone and a hysteresis around the threshold to control the dynamic offloading. Our extensive evaluations with public vSLAM data sets indicate that DynNetSLAM with the hysteresis substantially reduces the probability of track loss events compared to the state-of-the-art ORB-SLAM2 approach for processing statically on the mobile device and the enhanced static Edge SLAM. Also, DynNetSLAM nearly attains the low absolute position error and only slightly increases the CPU utilization compared to the enhanced static Edge SLAM.

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Section: A Motivation For Slam Network Offloadingmentioning

confidence: 97%

Section: B Offloading Slam Computingmentioning

confidence: 99%

DynNetSLAM: Dynamic Visual SLAM Network Offloading

et al. 2022

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“…The process SLAM uses is identifying an unfamiliar environment and localizing while referencing through a map. SLAM's objective is to update the position of the equipment while understanding its environment (Kuzmin, 2018;Sossalla et al, 2021).…”

Section: Related Literaturementioning

confidence: 99%

Accuracy Assessment of a Slam-Acquired Point Cloud Data Using a Variety of Classification Applications

Ingles,

Legaspi,

Sarmiento

et al. 2024

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Laser scanning techniques, such as Simultaneous Localization and Mapping (SLAM), produce three-dimensional data representing the real world, which may provide significant information for Building Information Models (BIM). These processes produce 3D point clouds, which require classification before being used in various applications such as structural assessments. However, most widely available software applications for classifying 3D point clouds are proprietary, giving an incomplete depiction of how the data is manipulated and processed. Thus, this research aims to assess the accuracy of the different classification applications in classifying the 3D point cloud data and perform a comparative analysis of the results. Precision, Recall, F1-score, and Accuracy are the evaluation metrics used to assess the classified 3D point cloud data. Results for Precision and Recall show that some of the applications can classify a particular class, the Ground and Building classes. However, the overall performance of the classification method, which is evaluated through the F1-score, produced low values. Results for the F1-score demonstrate that these low values indicate low overall reliability of the classification results despite high values for Accuracy. Based on the conducted experiments, further research is suggested to investigate the effect of increasing dataset size and equalizing class sizes used in classification.

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“…The continuous operation of the modern V-SLAM techniques on mobile devices is often hindered due to its high computational power needs. Researchers have recently introduced the concept of offloading resource-intensive V-SLAM processing steps from mobile robots to edge computing as a promising solution to address this limitation [145]. In [146], Edge-SLAM is introduced as a solution to address the computationally demanding nature of V-SLAM.…”

Section: E Edge Computingmentioning

confidence: 99%

A Survey of Visual SLAM Methods

Sahili,

Hassan,

Sakhrieh

et al. 2023

IEEE Access

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In the evolving landscape of modern robotics, Visual SLAM (V-SLAM) has emerged over the past two decades as a powerful tool, empowering robots with the ability to navigate and map their surroundings. While these methods are traditionally confined to static environments, there has been a growing interest in developing V-SLAM to handle dynamic and realistic scenes. This survey offers a comprehensive overview of the current state-of-the-art V-SLAM methods, including their strengths and weaknesses. The paper also identifies the limitations of existing techniques and proposes potential research directions for future advancements. In addition, it provides an overview of commonly used datasets to evaluate the performance of V-SLAM methods. This survey sheds valuable insights into areas that need additional research to benefit V-SLAM development, including challenges related to limited scalability for systems with multiple agents, sensitivity to lighting changes, high computational cost, and performance issues in noisy environments.

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Evaluating the Advantages of Remote SLAM on an Edge Cloud

Cited by 12 publications

References 13 publications

DynNetSLAM: Dynamic Visual SLAM Network Offloading

DynNetSLAM: Dynamic Visual SLAM Network Offloading

Accuracy Assessment of a Slam-Acquired Point Cloud Data Using a Variety of Classification Applications

A Survey of Visual SLAM Methods

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