Indoor Smartphone SLAM with Learned Echoic Location Features
- URL: http://arxiv.org/abs/2210.08493v1
- Date: Sun, 16 Oct 2022 09:41:09 GMT
- Title: Indoor Smartphone SLAM with Learned Echoic Location Features
- Authors: Wenjie Luo, Qun Song, Zhenyu Yan, Rui Tan, Guosheng Lin
- Abstract summary: We present a new indoor simultaneous localization and mapping (SLAM) system that uses a 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 ELF-based SLAM achieves median localization errors of $0.1,textm$, $0.53,textm$, and $0.5,textm$ on the reconstructed trajectories in a living room, an office, and a shopping mall
- Score: 47.264724701407545
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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 $0.1\,\text{m}$, $0.53\,\text{m}$, and $0.4\,\text{m}$ on the
reconstructed trajectories in a living room, an office, and a shopping mall,
and outperforms the Wi-Fi and geomagnetic SLAM systems.
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