# GEN-SLAM: Generative Modeling for Monocular Simultaneous Localization and Mapping

arXiv: https://arxiv.org/abs/1902.02086 · HTML: https://mlsanity.com/paper/1902.02086/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Feb 2019.

## Built on by (most cited first, 3 of 3)

- [An A* Curriculum Approach to Reinforcement Learning for RGBD Indoor Robot Navigation](https://mlsanity.com/paper/2101.01774/) (2021)
- [Improving Depth Estimation using Location Information](https://mlsanity.com/paper/2112.13925/) (2021)
- [V-LinkNet: Learning Contextual Inpainting Across Latent Space of Generative Adversarial Network](https://mlsanity.com/paper/2201.00323/) (2022)

## Abstract (arXiv)

We present a Deep Learning based system for the twin tasks of localization and obstacle avoidance essential to any mobile robot. Our system learns from conventional geometric SLAM, and outputs, using a single camera, the topological pose of the camera in an environment, and the depth map of obstacles around it. We use a CNN to localize in a topological map, and a conditional VAE to output depth for a camera image, conditional on this topological location estimation. We demonstrate the effectiveness of our monocular localization and depth estimation system on simulated and real datasets.