TY - JOUR
T1 - Controlling a robotic stereo camera under image quantization noise
AU - Freundlich, Charles
AU - Zhang, Yan
AU - Zhu, Alex Zihao
AU - Mordohai, Philippos
AU - Zavlanos, Michael M.
N1 - Publisher Copyright:
© SAGE Publications.
PY - 2017/10/1
Y1 - 2017/10/1
N2 - In this paper, we address the problem of controlling a mobile stereo camera under image quantization noise. Assuming that a pair of images of a set of targets is available, the camera moves through a sequence of Next-Best-Views (NBVs), i.e. a sequence of views that minimizes the trace of the targets' cumulative state covariance, constructed using a realistic model of the stereo rig that captures image quantization noise and a Kalman Filter (KF) that fuses the observation history with new information. The proposed algorithm decomposes control into two stages: first the NBV is computed in the camera relative coordinates, and then the camera moves to realize this view in the fixed global coordinate frame. This decomposition allows the camera to drive to a new pose that effectively realizes the NBV in camera coordinates while satisfying Field-of-View constraints in global coordinates, a task that is particularly challenging using complex sensing models. We provide simulations and real experiments that illustrate the ability of the proposed mobile camera system to accurately localize sets of targets. We also propose a novel data-driven technique to characterize unmodeled uncertainty, such as calibration errors, at the pixel level and show that this method ensures stability of the KF.
AB - In this paper, we address the problem of controlling a mobile stereo camera under image quantization noise. Assuming that a pair of images of a set of targets is available, the camera moves through a sequence of Next-Best-Views (NBVs), i.e. a sequence of views that minimizes the trace of the targets' cumulative state covariance, constructed using a realistic model of the stereo rig that captures image quantization noise and a Kalman Filter (KF) that fuses the observation history with new information. The proposed algorithm decomposes control into two stages: first the NBV is computed in the camera relative coordinates, and then the camera moves to realize this view in the fixed global coordinate frame. This decomposition allows the camera to drive to a new pose that effectively realizes the NBV in camera coordinates while satisfying Field-of-View constraints in global coordinates, a task that is particularly challenging using complex sensing models. We provide simulations and real experiments that illustrate the ability of the proposed mobile camera system to accurately localize sets of targets. We also propose a novel data-driven technique to characterize unmodeled uncertainty, such as calibration errors, at the pixel level and show that this method ensures stability of the KF.
KW - Range sensing
KW - mapping
KW - motion control
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U2 - 10.1177/0278364917735163
DO - 10.1177/0278364917735163
M3 - Article
AN - SCOPUS:85032439579
SN - 0278-3649
VL - 36
SP - 1268
EP - 1285
JO - International Journal of Robotics Research
JF - International Journal of Robotics Research
IS - 12
ER -