Evaluation of distance-aware KinFu algorithm for stereo outdoor data

In this paper, we report on experiments on deployment of an extended distance-aware KinFu algorithm, designed to generate 3D model from Kinect data, onto depth frames extracted from stereo camera data. The proposed idea allows to suppress the Kinect usage limitation for outdoor sensing due to the IR interference with sunlight. Besides this, exploiting the stereo data enables a hybrid 3D reconstruction system capable of switching between the Kinect depth frames and stereo data depending on the quality and quantity of the 3D and visual features on a scene. While the nature of the stereo sensing and the Kinect depth sensing is completely different, the stereo camera and the Kinect show similar sensitivity to distance capturing. We have evaluated the stereo-based 3D reconstruction with the extended KinFu algorithm with the following distance aware weighting strategies: (a) weight definition to prioritize importance of the sensed data depending on its accuracy, and (b) model updating to decide about the level of influence of the new data on the existing 3D model. The qualitative comparison of the resulting outdoor 3D models shows higher accuracy and smoothness of models obtained by introduced distance-aware strategies. The quantitative analysis reveals that applying the proposed weighting strategies onto stereo datasets enables to increase robustness of the pose-estimation algorithm and its endurance by factor of two.