Extracting Sensor Noise Models – considering X / Y and Z Noise

Thomas Köppel

Supervisor(s): Dr. Stefan Ohrhallinger

TU Wien

Abstract: Depth maps or point clouds extracted by depth sensors are prone to have errors. The goal of this work is the extraction of these errors (the noise) and a statistical estimation using a noise model. We have combined the sensor noise model described in this work and the noise model described in the work by Grossmann et al., generating a single noise model allowing a prediction of the amount of noise in specific areas of an image at a certain distance and rotation. We have conducted two test setups and measured the noise from 900 mm to 3.100 mm for the generation of the noise models. The test setup of this work focuses on determining the noise in X, Y and Z direction, covering the whole frustum of the respective depth sensor. Z noise was measured against a wall and X and Y noises were measured using a 3D chequerboard that was shifted through the room, allowing the above-mentioned coverage of the whole frustum. Along the edges of the cells of the chequerboard, the X and Y noise was measured. The combined model was evaluated by using a solid cube to classify the quality of our noise model. The estimation of the noise is important for applications like robot navigation that use data from depth sensors or when reconstructing a 3D scene captured by a depth sensor.
Keywords: 3D Reconstruction, Image Processing
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Year: 2018