SEESys: Online Pose Error Estimation System for Visual SLAM

Tianyi Hu, Tim Scargill, Fan Yang, Ying Chen, Guohao Lan, Maria Gorlatova

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

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Abstract

In this work, we introduce SEESys, the first system to provide online pose error estimation for Simultaneous Localization and Mapping (SLAM). Unlike prior offline error estimation approaches, the SEESys framework efficiently collects real-time system features and delivers accurate pose error magnitude estimates with low latency. This enables real-time quality-of-service information for downstream applications. To achieve this goal, we develop a SLAM system run-time status monitor (RTS monitor) that performs feature collection with minimal overhead, along with a multi-modality attention-based Deep SLAM Error Estimator (DeepSEE) for error estimation. We train and evaluate SEESys using both public SLAM benchmarks and a diverse set of synthetic datasets, achieving an RMSE of 0.235 cm of pose error estimation, which is 15.8% lower than the baseline. Additionally, we conduct a case study showcasing SEESys in a real-world scenario, where it is applied to a real-time audio error advisory system for human operators of a SLAM-enabled device. The results demonstrate that SEESys provides error estimates with an average end-to-end latency of 37.3 ms, and the audio error advisory reduces pose tracking error by 25%.

Original languageEnglish
Title of host publicationSenSys 2024 - Proceedings of the 2024 ACM Conference on Embedded Networked Sensor Systems
PublisherACM
Pages322-335
Number of pages14
ISBN (Electronic)9798400706974
DOIs
Publication statusPublished - 2024
Event22nd ACM Conference on Embedded Networked Sensor Systems, SenSys 2024 - Zhejiang University, Hangzhou, China
Duration: 4 Nov 20247 Nov 2024
https://sensys.acm.org/2024/

Publication series

NameSenSys 2024 - Proceedings of the 2024 ACM Conference on Embedded Networked Sensor Systems

Conference

Conference22nd ACM Conference on Embedded Networked Sensor Systems, SenSys 2024
Abbreviated titleSenSys 2024
Country/TerritoryChina
CityHangzhou
Period4/11/247/11/24
Internet address

Keywords

  • deep learning
  • edge computing
  • error estimate
  • pose tracking
  • SLAM
  • tracking error

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