A quantitative analysis of system bottlenecks in visual SLAM

Semenova, Sofiya; Ko, Steven Y.; Liu, Yu David; Ziarek, Lukasz; Dantu, Karthik

doi:10.1145/3508396.3512882

Cited by 6 publications

(3 citation statements)

References 9 publications

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“…Our map implementation already addresses the concurrency challenge in [3] but can be extended to address the timeliness challenge as well. To ensure that the delay between requesting and applying a write does not negatively impact system performance, our map implementation can be extended to increase throughput (e.g., by composing similar writes) and/or to implement various consistency models.…”

Section: Safe Concurrencymentioning

confidence: 99%

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A modular, extensible framework for modern visual SLAM systems

Semenova

Meshram

Chase

et al. 2022

Proceedings of the 20th Annual International Conference on Mobile Systems, Applications and Services

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Visual SLAM is a long-standing research area with many significant advances over the years. New systems typically build on previous contributions, but this requires significant development overhead, a highly detailed understanding of previous system implementations, and is rife with programming pitfalls. To enable fast experimentation and reduce the need for researchers to re-invent the wheel, we propose an extensible Visual SLAM framework with three features: modularity, seamless edge offloading, and safe concurrency. CCS Concepts• Computer systems organization → Embedded and cyberphysical systems; • Human-centered computing → Ubiquitous and mobile computing systems and tools; • Software and its engineering → Abstraction, modeling and modularity.

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Section: Safe Concurrencymentioning

confidence: 99%

“…We expand on recent work [3] that identified three system challenges to building consistent, accurate, and robust SLAM systems: timeliness, concurrency, and context awareness. As such, we additionally clarify how our framework can be extended to address these challenges.…”

Section: Introductionmentioning

confidence: 99%

A modular, extensible framework for modern visual SLAM systems

Semenova

Meshram

Chase

et al. 2022

Proceedings of the 20th Annual International Conference on Mobile Systems, Applications and Services

Self Cite

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“…By reducing the execution times, the shared data structures are blocked for a shorter period of time, further reducing the execution time. Semenova et al [43] present a quantitative analysis of the system challenges of ORB-SLAM2. They analysed the calculation times of the different ORB-SLAM2 modules on two computers with different processing powers.…”

Section: B Offloading Slam Computingmentioning

confidence: 99%

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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