# LatentSLAM: unsupervised multi-sensor representation learning for localization and mapping

arXiv: https://arxiv.org/abs/2105.03265 · HTML: https://mlsanity.com/paper/2105.03265/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 31 references that are in the map.
- First posted on arXiv 7 May 2021.

## Builds on (most cited first, 10 of 10)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Auto-Encoding Variational Bayes](https://mlsanity.com/paper/1312.6114/) (2013)
- [ORB-SLAM: a Versatile and Accurate Monocular SLAM System](https://mlsanity.com/paper/1502.00956/) (2015)
- [Do Better ImageNet Models Transfer Better?](https://mlsanity.com/paper/1805.08974/) (2018)
- [Past, Present, and Future of Simultaneous Localization And Mapping: Towards the Robust-Perception Age](https://mlsanity.com/paper/1606.05830/) (2016)
- [Deep Learning Features at Scale for Visual Place Recognition](https://mlsanity.com/paper/1701.05105/) (2017)
- [Are We Ready for Service Robots? The OpenLORIS-Scene Datasets for Lifelong SLAM](https://mlsanity.com/paper/1911.05603/) (2019)
- [Learning Perception and Planning with Deep Active Inference](https://mlsanity.com/paper/2001.11841/) (2020)
- [ViTa-SLAM: A Bio-inspired Visuo-Tactile SLAM for Navigation while Interacting with Aliased Environments](https://mlsanity.com/paper/1906.06422/) (2019)

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

- [Fusing Event-based Camera and Radar for SLAM Using Spiking Neural Networks with Continual STDP Learning](https://mlsanity.com/paper/2210.04236/) (2022)
- [Maximum Likelihood Learning of Latent Dynamics Without Reconstruction](https://mlsanity.com/paper/2505.23569/) (2025)
- [Home Run: Finding Your Way Home by Imagining Trajectories](https://mlsanity.com/paper/2208.10914/) (2022)
- [Towards bio-inspired unsupervised representation learning for indoor aerial navigation](https://mlsanity.com/paper/2106.09326/) (2021)
- [J-LAW: Joint Localization and Action-Conditioned World Modeling via Coupled Latent Factor Graphs](https://mlsanity.com/paper/2606.28712/) (2026)

## Abstract (arXiv)

Biologically inspired algorithms for simultaneous localization and mapping (SLAM) such as RatSLAM have been shown to yield effective and robust robot navigation in both indoor and outdoor environments. One drawback however is the sensitivity to perceptual aliasing due to the template matching of low-dimensional sensory templates. In this paper, we propose an unsupervised representation learning method that yields low-dimensional latent state descriptors that can be used for RatSLAM. Our method is sensor agnostic and can be applied to any sensor modality, as we illustrate for camera images, radar range-doppler maps and lidar scans. We also show how combining multiple sensors can increase the robustness, by reducing the number of false matches. We evaluate on a dataset captured with a mobile robot navigating in a warehouse-like environment, moving through different aisles with similar appearance, making it hard for the SLAM algorithms to disambiguate locations.