Neural RF SLAM for unsupervised positioning and mapping with channel
state information
- URL: http://arxiv.org/abs/2203.08264v1
- Date: Tue, 15 Mar 2022 21:32:44 GMT
- Title: Neural RF SLAM for unsupervised positioning and mapping with channel
state information
- Authors: Shreya Kadambi, Arash Behboodi, Joseph B. Soriaga, Max Welling,
Roohollah Amiri, Srinivas Yerramalli, Taesang Yoo
- Abstract summary: We present a neural network architecture for jointly learning user locations and environment mapping up to isometry.
The proposed model learns an interpretable latent, i.e., user location, by just enforcing a physics-based decoder.
- Score: 51.484516640867525
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: We present a neural network architecture for jointly learning user locations
and environment mapping up to isometry, in an unsupervised way, from channel
state information (CSI) values with no location information. The model is based
on an encoder-decoder architecture. The encoder network maps CSI values to the
user location. The decoder network models the physics of propagation by
parametrizing the environment using virtual anchors. It aims at reconstructing,
from the encoder output and virtual anchor location, the set of time of flights
(ToFs) that are extracted from CSI using super-resolution methods. The neural
network task is set prediction and is accordingly trained end-to-end. The
proposed model learns an interpretable latent, i.e., user location, by just
enforcing a physics-based decoder. It is shown that the proposed model achieves
sub-meter accuracy on synthetic ray tracing based datasets with single anchor
SISO setup while recovering the environment map up to 4cm median error in a 2D
environment and 15cm in a 3D environment
Related papers
- HYVE: Hybrid Vertex Encoder for Neural Distance Fields [9.40036617308303]
We present a neural-network architecture suitable for accurate encoding of 3D shapes in a single forward pass.
Our network is able to output valid signed distance fields without explicit prior knowledge of non-zero distance values or shape occupancy.
arXiv Detail & Related papers (2023-10-10T14:07:37Z) - V-DETR: DETR with Vertex Relative Position Encoding for 3D Object
Detection [73.37781484123536]
We introduce a highly performant 3D object detector for point clouds using the DETR framework.
To address the limitation, we introduce a novel 3D Relative Position (3DV-RPE) method.
We show exceptional results on the challenging ScanNetV2 benchmark.
arXiv Detail & Related papers (2023-08-08T17:14:14Z) - Object-level 3D Semantic Mapping using a Network of Smart Edge Sensors [25.393382192511716]
We extend a multi-view 3D semantic mapping system consisting of a network of distributed edge sensors with object-level information.
Our method is evaluated on the public Behave dataset where it shows pose estimation within a few centimeters and in real-world experiments with the sensor network in a challenging lab environment.
arXiv Detail & Related papers (2022-11-21T11:13:08Z) - NAF: Neural Attenuation Fields for Sparse-View CBCT Reconstruction [79.13750275141139]
This paper proposes a novel and fast self-supervised solution for sparse-view CBCT reconstruction.
The desired attenuation coefficients are represented as a continuous function of 3D spatial coordinates, parameterized by a fully-connected deep neural network.
A learning-based encoder entailing hash coding is adopted to help the network capture high-frequency details.
arXiv Detail & Related papers (2022-09-29T04:06:00Z) - iSDF: Real-Time Neural Signed Distance Fields for Robot Perception [64.80458128766254]
iSDF is a continuous learning system for real-time signed distance field reconstruction.
It produces more accurate reconstructions and better approximations of collision costs and gradients.
arXiv Detail & Related papers (2022-04-05T15:48:39Z) - Semi-Supervised Learning for Channel Charting-Aided IoT Localization in
Millimeter Wave Networks [97.66522637417636]
A novel framework is proposed for channel charting (CC)-aided localization in millimeter wave networks.
In particular, a convolutional autoencoder model is proposed to estimate the three-dimensional location of wireless user equipment.
The framework is extended to a semi-supervised framework, where the autoencoder is divided into two components.
arXiv Detail & Related papers (2021-08-03T14:41:38Z) - PLADE-Net: Towards Pixel-Level Accuracy for Self-Supervised Single-View
Depth Estimation with Neural Positional Encoding and Distilled Matting Loss [49.66736599668501]
We propose a self-supervised single-view pixel-level accurate depth estimation network, called PLADE-Net.
Our method shows unprecedented accuracy levels, exceeding 95% in terms of the $delta1$ metric on the KITTI dataset.
arXiv Detail & Related papers (2021-03-12T15:54:46Z) - Wireless Localisation in WiFi using Novel Deep Architectures [4.541069830146568]
This paper studies the indoor localisation of WiFi devices based on a commodity chipset and standard channel sounding.
We present a novel shallow neural network (SNN) in which features are extracted from the channel state information corresponding to WiFi subcarriers received on different antennas.
arXiv Detail & Related papers (2020-10-16T22:48:29Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.