Laser-Tracker-Assisted Camera-to-Robot Calibration for Mobile Robots

Jan A. Rudolph; Öykü Kandemir; Markus Ulrich

cs.ROSep 22, 2026

Laser-Tracker-Assisted Camera-to-Robot Calibration for Mobile Robots

Authors: Jan A. Rudolph, Öykü Kandemir, Markus Ulrich

Abstract

We present a laser-tracker-assisted hand-eye calibration method for camera-equipped mobile robots. The method combines laser-tracker-based 3D metrology with camera-based 2D observations. Building on our previous laser-tracker-assisted camera-to-robot calibration method for ground-observing mobile robots, we present a generalized formulation for calibrating the camera pose in the coordinate system of tracker-localized mobile robots. The new approach relaxes assumptions of our previous method on robot and camera configuration by chaining multiple calibration targets resulting in a more general approach supporting various camera-equipped mobile robot systems.

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Robust Camera-to-Mocap Calibration and Verification for Large-Scale Multi-Camera Data Capture

Tianyi Liu, Christopher Twigg, Patrick Grady, Kevin Harris+2

Meta

Optical motion capture (mocap) systems are widely used for ground-truth capture in AR/VR, SLAM and robotics datasets. These datasets require extrinsic calibration to align mocap coordinates to external camera frames -- a step that is subject to multiple sources of error in practice, and failures often go undetected until they corrupt downstream data. These issues are compounded for fisheye cameras, where spatially non-uniform distortion makes both calibration and verification more challenging. We present a calibration and verification system designed for this setting. Concretely, we target robustness to board-to-marker attachment variation, optimization initialization ambiguity, and session-to-session calibration drift after deployment. The calibration jointly estimates camera extrinsics and the board-to-marker transform, and uses a staged solver to improve convergence reliability under ambiguous initialization. The verification component, \lollypop, provides fast, operator-independent assessment through a measurement chain entirely independent of the calibration data. In experiments on a Meta Quest 3 headset with fisheye cameras, our calibration outperforms existing benchwork, and lollypop reliably detects calibration degradation over time. The system has been deployed in production data collection pipelines.

Camera Intrinsics Multi-Camera Systems Motion Capture Simultaneous Localization And Mapping

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Abstract

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