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This article concerns the problem of a dense mapping system for a robot exploring a new environment. In this scenario, a robot equipped with an RGB-D camera uses RGB and range data to build a consistent model of the environment. Firstly, dense mapping requires the selection of the data representation. Secondly, the dense mapping system has to deal with localization drift which can be corrected when loop closure is detected. In this article, we deal with both of these problems, and we make several technical contributions. We define local maps which use the Normal Distribution Transform (NDT) stored in the 2D structures to represent the local scene with varying 3D resolution. This method directly utilizes the uncertainty model of the range sensor and provides information about the accuracy of the data in the map. We also propose an architecture that utilizes pose and covisibility graphs to correct a global model of the environment after loop closure detection. We show how to integrate the dense local mapping with the pose graph and keyframes management system in the ORB-SLAM2 localization. Finally, we show the advantages of the view-dependent model over the methods that uniformly divide the space to represent objects in the environment.

3D Dense Mapping with the Graph of Keyframe-Based and View-Dependent Local Maps