Fast 3D modeling in complex environments using a single Kinect sensor

Three-dimensional (3D) modeling technology has been widely used in inverse engineering, urban planning, robot navigation, and many other applications. How to build a dense model of the environment with limited processing resources is still a challenging topic. A fast 3D modeling algorithm that only uses a single Kinect sensor is proposed in this paper. For every color image captured by Kinect, corner feature extraction is carried out first. Then a spiral search strategy is utilized to select the region of interest (ROI) that contains enough feature corners. Next, the iterative closest point (ICP) method is applied to the points in the ROI to align consecutive data frames. Finally, the analysis of which areas can be walked through by human beings is presented. Comparative experiments with the well-known KinectFusion algorithm have been done and the results demonstrate that the accuracy of the proposed algorithm is the same as KinectFusion but the computing speed is nearly twice of KinectFusion. 3D modeling of two scenes of a public garden and traversable areas analysis in these regions further verified the feasibility of our algorithm.

• A fast 3D modeling algorithm in complex environments is proposed.
• The proposed system is compact and only uses a single Kinect sensor as the sensing device.
• The proposed algorithm is as accurate as the well-known KinectFusion algorithm, but the processing speed is almost twice of KinectFusion.
• The algorithm is time-efficient and can be applied in real-time applications with limited computing resources.
• A method for analyzing traversable areas for human beings is proposed, which can provide essential information to urban planners.