# RGBDTAM: A Cost-Effective and Accurate RGB-D Tracking and Mapping System

## TL;DR

This paper introduces RGBDTAM, a cost-effective RGB-D SLAM system that achieves state-of-the-art accuracy and robustness in real-time CPU operation by combining semi-dense photometric and dense geometric errors with multi-view constraints.

## Contribution

The paper presents a novel RGB-D SLAM algorithm that improves accuracy and robustness using a combined error model and multi-view constraints, outperforming GPU-based systems in CPU-based real-time scenarios.

## Key findings

- Better accuracy and robustness than GPU-based systems
- Effective real-time CPU operation
- Open-source implementation available

## Abstract

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 approach 1 . The reader is referred to a video with our results 2 for a more illustrative visualization of its performance.