# Indoor Smartphone SLAM with Learned Echoic Location Features

arXiv: https://arxiv.org/abs/2210.08493 · HTML: https://mlsanity.com/paper/2210.08493/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 64 references that are in the map.
- First posted on arXiv 16 Oct 2022.

## Builds on (most cited first, 9 of 9)

- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Big Self-Supervised Models are Strong Semi-Supervised Learners](https://mlsanity.com/paper/2006.10029/) (2020)
- [Pyroomacoustics: A Python package for audio room simulations and array processing algorithms](https://mlsanity.com/paper/1710.04196/) (2017)
- [LCDNet: Deep Loop Closure Detection and Point Cloud Registration for LiDAR SLAM](https://mlsanity.com/paper/2103.05056/) (2021)
- [An Analysis of the t-SNE Algorithm for Data Visualization](https://mlsanity.com/paper/1803.01768/) (2018)
- [Efficient Continuous-Time SLAM for 3D Lidar-Based Online Mapping](https://mlsanity.com/paper/1810.06802/) (2018)
- [Mic2Mic: Using Cycle-Consistent Generative Adversarial Networks to Overcome Microphone Variability in Speech Systems](https://mlsanity.com/paper/2003.12425/) (2020)
- [Ubiquitous Acoustic Sensing on Commodity IoT Devices: A Survey](https://mlsanity.com/paper/1901.03450/) (2019)
- [Deep Room Recognition Using Inaudible Echos](https://mlsanity.com/paper/1809.00531/) (2018)

## Built on by (most cited first, 1 of 1)

- [Map++: Towards User-Participatory Visual SLAM Systems with Efficient Map Expansion and Sharing](https://mlsanity.com/paper/2411.02553/) (2024)

## Abstract (arXiv)

Indoor self-localization is a highly demanded system function for smartphones. The current solutions based on inertial, radio frequency, and geomagnetic sensing may have degraded performance when their limiting factors take effect. In this paper, we present a new indoor simultaneous localization and mapping (SLAM) system that utilizes the smartphone's built-in audio hardware and inertial measurement unit (IMU). Our system uses a smartphone's loudspeaker to emit near-inaudible chirps and then the microphone to record the acoustic echoes from the indoor environment. Our profiling measurements show that the echoes carry location information with sub-meter granularity. To enable SLAM, we apply contrastive learning to construct an echoic location feature (ELF) extractor, such that the loop closures on the smartphone's trajectory can be accurately detected from the associated ELF trace. The detection results effectively regulate the IMU-based trajectory reconstruction. Extensive experiments show that our ELF-based SLAM achieves median localization errors of 0.1,m, 0.53,m, and 0.4,m on the reconstructed trajectories in a living room, an office, and a shopping mall, and outperforms the Wi-Fi and geomagnetic SLAM systems.