A novel hand-eye calibration method for ground-observing mobile robots is proposed. While cameras on mobile robots are common, they are rarely used for ground-observing measurement tasks. Laser trackers are increasingly used in robotics for precise localization. A referencing plate is designed to combine the two measurement modalities of laser-tracker 3D metrology and camera-based 2D imaging. It incorporates reflector nests for pose acquisition using a laser tracker and a camera calibration target that is observed by the robot-mounted camera. The procedure comprises estimating the plate pose, the plate-camera pose, and the robot pose, followed by computing the robot-camera transformation. Experiments indicate sub-millimeter repeatability.
Figures & tables
Figure 1: Mobile robot Rita (adapted from Naab (2023) ), used in the proposed experiments. It features a differential drive with a front-mounted caster wheel. A spherically mounted retro-reflector is installed on top within a rotatable mount, maintaining the line of sight to a laser tracker. The nadir-mounted camera remains occluded in this image, with only its active illumination apparent.
Figure 2: Retrieval of the orientation of the camera inside the robot frame. The plate-absolute-transformation absHplt and the plate-camera-transformation pltHcam , as well as the robot-absolute-transformation absHrob on the other side, are combined to robot-camera-transformation robHcam (figure adapted from Ulrich and Hillemann (2024) ).
Figure 3: Stereo measurement of the reflector nest positions in the referencing plate frame. The plate-camera-transformations pltHcam,0 and pltHcam,1 are used to calculate the reflector nest positions pltPi by triangulation (turquoise arrows) (figure adapted from Ulrich and Hillemann (2024) ).
Figure 4: Retrieval of the absolute orientation of the referencing plate. Absolute laser tracker measurements absPi are matched to the reflector nest positions w.r.t. the referencing plate, yielding the absolute-plate-transformation absHplt (figure adapted from Ulrich and Hillemann (2024) ).
Figure 5: Referencing plates used in experiments. While they are arbitrarily posed on the ground, a mobile robot Rita can be placed on them. The colored circles mark flush-mounted reflector nests. The dark texture represents the HALCON calibration target with circular marks. The rest of the white area provides space to move the robot for a few decimeters.
Figure 6: Results of repeated absolute measurements of marks on the floor in an experiment. Each plot is normalized to the overall mean position, see Table 1 . (a) and (c) denote the same point as Table 1 does. (b) and (d) denote another mark on the floor. (a) and (b) are based on the in-house-designed wooden referencing plate. (c) and (d) are based on the bespoke glass referencing plate
Direction
Mean Position [mm]
Distance Metrics [mm]
Approach Angle Range [ ∘ ]
X
Y
Max from Mean
Mean from Mean
Cluster Radius
left
130825.822
8525.577
0.131
0.098
0.117
[89.48, 90.66]
right
130826.054
8525.014
0.119
0.094
0.109
[-90.40, -89.96]
up
130825.853
8525.427
0.188
0.140
0.163
[0.12, 0.36]
down
130826.224
8525.161
0.054
0.042
0.052
[179.53, 179.68]
upleft
130825.905
8525.413
0.192
0.139
0.150
[45.00, 45.06]
Table 1: Quantitative results of repeated absolute measurements of marks on the floor in an experiment. Individual (non-averaged) measured positions of this experiment are plotted in 6(c) . Mean position is the mean of the measured absolute coordinates of the respective cluster. Max. and mean from mean denote the (max. and mean, respectively) distance to this mean position . Cluster diameter is the max. distance between two points in the cluster. In the last row, those metrics are calculated for all directions together.
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.
Jan A. Rudolph, Öykü Kandemir, Markus Ulrich
Institute of Photogrammetry and Remote Sensing (IPF), Karlsruhe Institute of Technology (KIT), Englerstr. 7, 76131 Karlsruhe, Germany
Camera-to-motion-capture (mocap) calibration is essential for using mocap as ground truth in robotics, AR/VR, and other computer vision tasks. However, the calibration can drift after deployment, while calibration residuals and visual inspection provide limited independent verification. We present Lollypop, a fiducial-mocap reference target for independent calibration verification. The target couples an ArUco fiducial with a mocap marker constellation so the visual center and tracked centroid represent the same physical point. Given a candidate calibration, verification projects the mocap point into the image and measures its disagreement with the detected fiducial center. Experiments show sub-pixel nominal error, sensitivity to controlled extrinsic perturbations, and increasing error during an illustrative mixed-handling sequence.
Mobile robotic platforms offer a flexible alternative to fixed manipulators for non-destructive evaluation (NDE) of large aerospace structures, but their base-positioning accuracy and how that accuracy should inform deployment have not been assessed under a common, externally referenced protocol. This work presents a laser tracker-based evaluation workflow (ground truth approximately 6 micrometers) that measures the static and segmented trajectory positioning accuracy of five commercial mobile platforms (KUKA KMP-1500, KUKA KMR, MiR250, Boston Dynamics Spot, Clearpath Husky) under a common protocol. A coupled multi-corner calibration recovers the laser-to-robot transformation and reflector offsets; ordinary least squares over all poses is used, with robust estimation retained only as a blunder check. Static positioning accuracy ranged from a median of 8.2 mm (KMP-1500) to 63.5 mm (Spot), with the wheel-odometry-only Husky uncalibratable. Dynamic path following was characterised by cross-track error; the component was insensitive to temporal alignment, which ranged from 6.9 mm (KMP-1500) to 112.1 mm (Spot). Both accuracy and calibratability tracked localisation capability, from the newest LiDAR SLAM platform to map-free visual odometry. No configuration meets the 0.2 to 1.0 mm aerospace NDE tolerance from the base alone; the results are framed as a design input that sizes the supplementary sensing each platform requires: roughly one order of magnitude for the best platform and nearly two for the worst, providing a reproducible basis for platform selection rather than a feasibility claim.
SeyedMohammadAmin Nabi Pour, S. Gareth Pierce, Randika Vithanage +3