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.

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Aquaculture net cage inspection and maintenance is a central issue in fish farming. Inspection using autonomous underwater vehicles is a promising solution. This paper proposes laser-camera triangulation for pose estimation to enable autonomous net following for an autonomous vehicle. The laser triangulation 3D data is experimentally compared to a doppler velocity log (DVL) in an active fish farm. We show that our system is comparable in performance to a DVL for distance and angular pose measurements. Laser triangulation is promising as a short distance ranging sensor for autonomous vehicles at a low cost compared to acoustic sensors.

High-precision underwater 3D cameras are required to automate many of the traditional subsea inspection, maintenance and repair (IMR) operations. In this paper we introduce a novel multi-frequency phase stepping (structured light) method for high-precision 3D estimation even in turbid water. We introduce an adaptive phase-unwrapping procedure which uses the phase-uncertainty to determine the highest frequency that can be reliably unwrapped. Light scattering adversely affects the phase estimate. We propose to remove the effect of forward scatter with an unsharp filter and a model-based method to remove the backscatter effect. Tests in varying turbidity show that the scatter correction removes the adverse effect of scatter on the phase estimates. The adaptive frequency unwrapping with scatter correction results in images with higher accuracy and precision and less phase unwrap errors than the Gray-Code Phase Stepping (GCPS) approach.