Stabilization of 3D Pavement Images for Pothole Metrology Using the Kalman Filter

Roads are scanned with automated imagers for distresses on a regular basis. Motion of imaging platforms introduces instability in pavement images during image acquisition process, thus, provides less accurate measurements. In this paper, a novel approach is proposed to stabilize 3D pavement images using the Kalman filter with affine transformations as a state space model. The vibration of the imaging platform is taken using a simulated accelerometer and used as a significant feature to measure the effects of instability in pavement images. A Simulink model is presented in this regard to demonstrate the stabilization technique. This paper is an extension of our previous work on metrology and visualization of potholes using Kinect sensor. The effects of vibration for a displacement range of 0.2-1 mm are studied for pure translations in 3D pavement images. The Kalman filter shows a promising future for stabilization of 3D pavement images. Calculations using values from Kalman filter show a reduction in error from 5.35% to 3.76% and from 5.38% to 3.09% for volume and perimeter estimations respectively.