RGBDTAM: A cost-effective and accurate RGB-D tracking and mapping system

Simultaneous Localization and Mapping using RGB-D cameras has been a fertile research topic in the latest decade, due to the suitability of such sensors for indoor robotics. In this paper we propose a direct RGB-D SLAM algorithm with state-of-the-art accuracy and robustness at a los cost. Our experiments in the RGB-D TUM dataset [34] effectively show a better accuracy and robustness in CPU real time than direct RGB-D SLAM systems that make use of the GPU. The key ingredients of our approach are mainly two. Firstly, the combination of a semi-dense photometric and dense geometric error for the pose tracking (see Figure 1), which we demonstrate to be the most accurate alternative. And secondly, a model of the multi-view constraints and their errors in the mapping and tracking threads, which adds extra information over other approaches. We release the open-source implementation of our approach¹. The reader is referred to a video with our results ² for a more illustrative visualization of its performance.

I. Introduction

The availability of affordable and accurate RGB-D cameras has caused a profound impact in mobile robotics. Currently, the research lines based on such technology are as varied as object recognition [23], scene recognition and understanding [14], [31], person detection [32] or human-robot interfaces [35].