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arXiv Robotics — research abstracts· Haifa Zhang, Yijing Wang, Zhiqiang Zuo·· 3 hours agoSignalEditorial score75

CalibSwitch: Observation-Driven Camera-LiDAR Extrinsic Recovery and Selective Maintenance for Multimodal Perception

CalibSwitch: Observation-Driven Camera-LiDAR Extrinsic Recovery and Selective Maintenance for Multimodal Perception

Summary

This research presents CalibSwitch, an observation-driven framework that separates absolute extrinsic recovery from selective state maintenance for camera-LiDAR perception on mobile robots.

Editorial context

The paper introduces CalibSwitch, a framework combining a cross-modal absolute extrinsic generator and a selective recurrent calibration policy to maintain sensor geometry on mobile robots without relying on numerical initial transforms or unconditional per-frame updates.

Source: arXiv Robotics — research abstracts · Read original article ↗

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What the source reports

Publisher-reported claims, with original evidence. These results have not been independently verified by RoboSignal.

Reported numbers

  • mean translation error

    0.248

    View original evidence
    d temporal history to determine when a candidate should replace the working geometry consumed by a frozen multimodal detector. On nuScenes, CAEG achieves mean translation and rotation errors of 0.248 m and 5.884$^\circ$. Under sustained geometric transitions, SRCP reaches a mean composite error of 1.46 with 9.70\% candidate commitments, outperforming quality-based and temporal-consistency gating at comparable commitm
    Open source S4
  • mean rotation error

    5.884

    View original evidence
    d temporal history to determine when a candidate should replace the working geometry consumed by a frozen multimodal detector. On nuScenes, CAEG achieves mean translation and rotation errors of 0.248 m and 5.884$^\circ$. Under sustained geometric transitions, SRCP reaches a mean composite error of 1.46 with 9.70\% candidate commitments, outperforming quality-based and temporal-consistency gating at comparable commitm
    Open source S4
  • mean composite error

    1.46

    View original evidence
    d temporal history to determine when a candidate should replace the working geometry consumed by a frozen multimodal detector. On nuScenes, CAEG achieves mean translation and rotation errors of 0.248 m and 5.884$^\circ$. Under sustained geometric transitions, SRCP reaches a mean composite error of 1.46 with 9.70\% candidate commitments, outperforming quality-based and temporal-consistency gating at comparable commitm
    Open source S4
  • mAP improvement

    1.43

    View original evidence
    ent rates. Relative to unconditional per-frame updating, it improves frozen-detector mAP and NDS by 1.43 and 0.92 points. Paired geometry--observation uncertainty tests, two real-vehicle platforms, and 13.5-FPS single-camera Jetson execution demonstrate the system value of selective geometric-state maintenance for continuous perception.
    Open source S5
  • NDS improvement

    0.92

    View original evidence
    ent rates. Relative to unconditional per-frame updating, it improves frozen-detector mAP and NDS by 1.43 and 0.92 points. Paired geometry--observation uncertainty tests, two real-vehicle platforms, and 13.5-FPS single-camera Jetson execution demonstrate the system value of selective geometric-state maintenance for continuous perception.
    Open source S5
  • execution frame rate

    13.5

    View original evidence
    ent rates. Relative to unconditional per-frame updating, it improves frozen-detector mAP and NDS by 1.43 and 0.92 points. Paired geometry--observation uncertainty tests, two real-vehicle platforms, and 13.5-FPS single-camera Jetson execution demonstrate the system value of selective geometric-state maintenance for continuous perception.
    Open source S5
  • paper: not_reported
Source excerpts and review record

Automatically extracted; no manual editorial approval recorded.

d temporal history to determine when a candidate should replace the working geometry consumed by a frozen multimodal detector. On nuScenes, CAEG achieves mean translation and rotation errors of 0.248 m and 5.884$^\circ$. Under sustained geometric transitions, SRCP reaches a mean composite error of 1.46 with 9.70\% candidate commitments, outperforming quality-based and temporal-consistency gating at comparable commitm

Open source S4

ent rates. Relative to unconditional per-frame updating, it improves frozen-detector mAP and NDS by 1.43 and 0.92 points. Paired geometry--observation uncertainty tests, two real-vehicle platforms, and 13.5-FPS single-camera Jetson execution demonstrate the system value of selective geometric-state maintenance for continuous perception.

Open source S5

Source:arXiv Robotics — research abstracts · arxiv.org

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