Semi-direct tracking and mapping with RGB-D camera for MAV

In this paper we present a novel semi-direct tracking and mapping (SDTAM) approach for RGB-D cameras which inherits the advantages of both direct and feature based methods, and consequently it achieves high efficiency, accuracy, and robustness. The input RGB-D frames are tracked with a direct method and keyframes are refined by minimizing a proposed measurement residual function which takes both geometric and depth information into account. A local optimization is performed to refine the local map while global optimization detects and corrects loops with the appearance based bag of words and a co-visibility weighted pose graph. Our method has higher accuracy on both trajectory tracking and surface reconstruction compared to state-of-the-art frame-to-frame or frame-model approaches. We test our system in challenging sequences with motion blur, fast pure rotation, and large moving objects, the results demonstrate it can still successfully obtain results with high accuracy. Furthermore, the proposed approach achieves real-time speed which only uses part of the CPU computation power, and it can be applied to embedded devices such as phones, tablets, or micro aerial vehicles (MAVs).