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

arXiv: https://arxiv.org/abs/1703.00754 · HTML: https://mlsanity.com/paper/1703.00754/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Mar 2017.

## Built on by (most cited first, 5 of 5)

- [Instant Visual Odometry Initialization for Mobile AR](https://mlsanity.com/paper/2107.14659/) (2021)
- [Learned Monocular Depth Priors in Visual-Inertial Initialization](https://mlsanity.com/paper/2204.09171/) (2022)
- [Light-SLAM: A Robust Deep-Learning Visual SLAM System Based on LightGlue under Challenging Lighting Conditions](https://mlsanity.com/paper/2407.02382/) (2024)
- [A Model for Multi-View Residual Covariances based on Perspective Deformation](https://mlsanity.com/paper/2202.00765/) (2022)
- [S3E-GNN: Sparse Spatial Scene Embedding with Graph Neural Networks for Camera Relocalization](https://mlsanity.com/paper/2205.05861/) (2022)

## Abstract (arXiv)

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.