Range sensor based model construction by sparse surface adjustment

In this paper, we propose an approach to construct highly accurate 3D object models from range data. The main advantage of sensor based model acquisition compared to manual CAD model construction is the short time needed per object. The usual drawbacks of sensor based model reconstruction are sensor noise and errors in the sensor positions which typically lead to less accurate models. Our method drastically reduces this problem by applying a physical model of the underlying range sensor and utilizing a graph-based optimization technique. We present our approach and evaluate it on data recorded in different real world environments with an RGBD camera and a laser range scanner. The experimental results demonstrate that our method provides more accurate maps than standard SLAM methods and that it additionally compares favorable over the moving least squares method.