Modelling and compensating measurement errors caused by scattering in time-of-flight cameras

Abstract

Recently, Range Imaging (RIM) cameras have become available that capture high resolution range images at video rate. Such cameras measure the distance from the scene for each pixel independently based upon a measured time of flight (TOF). Some cameras, such as the SwissRanger™ SR-3000, measure the TOF based on the phase shift of reflected light from a modulated light source. Such cameras are shown to be susceptible to severe distortions in the measured range due to light scattering within the lens and camera. Earlier work induced using a simplified Gaussian point spread function and inverse filtering to compensate for such distortions. In this work a method is proposed for how to identify and use generally shaped empirical models for the point spread function to get a more accurate compensation. The otherwise difficult inverse problem is solved by using the forward model iteratively, according to well established procedures from image restoration. Each iteration is done as a sequential process, starting with the brightest parts of the image and then moving sequentially to the least bright parts, with each step subtracting the estimated effects from the measurements. This approach gives a faster and more reliable compensation convergence. An average reduction of the error by more than 60% is demonstrated on real images. The computation load corresponds to one or two convolutions of the measured complex image with a real filter of the same size as the image.

An integrated bundle adjustment approach to range camera geometric self-calibration

“…The reflected light from a foreground object undergoes multiple reflections within the camera, thereby introducing parasitic signals that bias the signals from the background targets (Kavli et al, 2008;Mure-Dubois and Hügli, 2007). These additive signals cause a shortening of the range measurements to the background objects due to the phase shift of the received signal.…”

Pipeline landmark detection for autonomous robot navigation using time-of-flight imagery

“…knowledge of the imaging process. For instance, some of the effects described in [4] could be incorporated, using a forward model instead of the described reverse-modeling of the effects. The estimated range to detected landmarks or obstacles is yet to be verified with better precision.…”

A motion based real-time foveation control loop for rapid and relevant 3D laser scanning

“…Due to their narrow laser spot illumination, time-of-flight laser scanners allow an effective suppression of background signals and multiple-reflection artifacts otherwise known to be problematic in time-offlight cameras [13]. Compared to stereo cameras, time-offlight systems have fewer problems with occlusions, due to their on-axis detection principle.…”