Stereoscopic Light Stripe Scanning: Interference Rejection, Error Minimization and Calibration

This paper addresses the problem of rejecting interference due to secondary specular reflections, cross-talk and other mechanisms in an active light stripe scanner for robotic applications. Conventional scanning methods control the environment to ensure the brightness of the stripe exceeds that of all other features. However, for a robot operating in an uncontrolled environment, valid measurements are likely to be hidden amongst noise. Robust scanning methods already exist, but suffer from problems including assumed scene structure, acquisition delay, lack of error recovery, and incorrect modelling of measurement noise. We propose a robust technique that overcomes these problems, using two cameras and knowledge of the light plane orientation to disambiguate the primary reflection from spurious measurements. Unlike other robust techniques, our validation and reconstruction algorithms are optimal with respect to sensor noise. Furthermore, we propose an image-based procedure to calibrate the system using measurements of an arbitrary non-planar target, providing robust validation independently of ranging accuracy. Finally, our robust techniques allow the sensor to operate in ambient indoor light, allowing colour and range to be implicitly registered. An experimental scanner demonstrates the effectiveness of the proposed techniques.