Highly accurate 3D surface models by sparse surface adjustment

In this paper, we propose an approach to obtain highly accurate 3D models from range data. The key idea of our method is to jointly optimize the poses of the sensor and the positions of the surface points measured with a range scanning device. Our approach applies a physical model of the underlying range sensor. To solve the optimization task it employs a state-of-the-art graph-based optimizer and iteratively refines the structure of the error function by recomputing the data associations after each optimization. We present our approach and evaluate it on data recorded in different real world environments with a RGBD camera and a laser range scanner. The experimental results demonstrate that our method is able to substantially improve the accuracy of SLAM results and that it compares favorable over the moving least squares method.