Toolbox Release: A WiFi-Based Relative Bearing Sensor for Robotics

Diana Zhang; Ninad Jadhav; Stephanie Gil; Swarun Kumar; Weiying Wang

RECORD

Toolbox Release: A WiFi-Based Relative Bearing Sensor for Robotics

Ninad Jadhav, Weiying Wang, Diana Zhang, Swarun Kumar, Stephanie Gil

Robotics · Submitted 24 Sep 2021

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim Empirical results show that the bearing estimation from our toolbox achieves mean accuracy of 5.10 degrees.

how it is stated · asserted 1.00 read the record →

Comment (0) Read on arXiv ↗

Cite ▾

arXiv ID2109.12205 (version 2)

Pith IDWVSCANRF, Pith's permanent record of this paper and its review

CitationDiana Zhang, Ninad Jadhav, Stephanie Gil, Swarun Kumar, Weiying Wang. "Toolbox Release: A WiFi-Based Relative Bearing Sensor for Robotics." arXiv:2109.12205 (2021). https://pith.science/paper/WVSCANRF

Claim paper

The compiled record

The paper's own sentences, arranged. Each label is drawn from a closed vocabulary; the number beside it is the probability the classifier gave that label. Numbers in the tables are the authors', checked by code. No sentence in this record was written by a model.

Open the compiled record

Compiled by classifier-1.13.0 via TypeSafe API, question bank abstract-p1-20260923, 2026-09-23: 1 request, 7,480 input tokens.

What the abstract itself states, as probabilities: claims first 0.74 · states limitation 0.24 · human participants 0.03 · controlled comparison 0.11 · randomized assignment 0.02 · states quantitative result 0.99 · compares with prior results 0.08 · mentions code or data availability 0.99.

The abstract, sentence by sentence

arXiv listing abstract v2. Results and the main claim are underlined; show every role.

This paper presents the WiFi-Sensor-for-Robotics (WSR) toolbox, an open source C++ framework. It enables robots in a team to obtain relative bearing to each other, even in non-line-of-sight (NLOS) settings which is a very challenging problem in robotics. It does so by analyzing the phase of their communicated WiFi signals as the robots traverse the environment. This capability, based on the theory developed in our prior works, is made available for the first time as an opensource tool. It is motivated by the lack of easily deployable solutions that use robots' local resources (e.g WiFi) for sensing in NLOS. This has implications for localization, ad-hoc robot networks, and security in multi-robot teams, amongst others. The toolbox is designed for distributed and online deployment on robot platforms using commodity hardware and on-board sensors. We also release datasets demonstrating its performance in NLOS and line-of-sight (LOS) settings for a multi-robot localization usecase. Empirical results show that the bearing estimation from our toolbox achieves mean accuracy of 5.10 degrees. This leads to a median error of 0.5m and 0.9m for localization in LOS and NLOS settings respectively, in a hardware deployment in an indoor office environment.

background contribution method result interpretation main claim

What the classifier reports for each sentence. The number is the probability it gave the label.
#	sentence	role	the authors' own work	states the main conclusion	strength
s0	This paper presents the WiFi-Sensor-for-Robotics (WSR) toolbox, an open source C++ framework.	contribution 1.00	0.74	0.06	asserted 1.00
s1	It enables robots in a team to obtain relative bearing to each other, even in non-line-of-sight (NLOS) settings which is a very challenging problem in robotics.	contribution 0.99	0.43	0.23	asserted 0.99
s2	It does so by analyzing the phase of their communicated WiFi signals as the robots traverse the environment.	method 1.00	0.59	0.07	asserted 1.00
s3	This capability, based on the theory developed in our prior works, is made available for the first time as an opensource tool.	contribution 0.87	0.55	0.12	asserted 1.00
s4	It is motivated by the lack of easily deployable solutions that use robots' local resources (e.g WiFi) for sensing in NLOS.	gap 0.96	0.11	0.03	asserted 0.99
s5	This has implications for localization, ad-hoc robot networks, and security in multi-robot teams, amongst others.	interpretation 1.00	0.16	0.07	asserted 0.63
s6	The toolbox is designed for distributed and online deployment on robot platforms using commodity hardware and on-board sensors.	contribution 0.59	0.43	0.04	asserted 1.00
s7	We also release datasets demonstrating its performance in NLOS and line-of-sight (LOS) settings for a multi-robot localization usecase.	contribution 0.98	0.91	0.06	asserted 0.99
s8	Empirical results show that the bearing estimation from our toolbox achieves mean accuracy of 5.10 degrees.	result 1.00	0.96	0.30	asserted 1.00
s9	This leads to a median error of 0.5m and 0.9m for localization in LOS and NLOS settings respectively, in a hardware deployment in an indoor office environment.	result 1.00	0.95	0.31	asserted 1.00

What kind of paper the classifier reports this is: method or tool 1.00 · evidence from observation or experiment 1.00.

What was examined, and what was not

sources read

arXiv listing abstract v2

not examined

full text
figures and tables
references and citing sentences
relations to other papers

levels run

P abstract

not run

S sections
T body sentences
C citations
R cross-paper relations

Every quoted sentence on this record is the authors'. Every label is from Pith's closed vocabulary, with the probability shown beside it. Every number in the measurements table is the authors', checked by code. Record as JSON

The paper

Original abstract

This paper presents the WiFi-Sensor-for-Robotics (WSR) toolbox, an open source C++ framework. It enables robots in a team to obtain relative bearing to each other, even in non-line-of-sight (NLOS) settings which is a very challenging problem in robotics. It does so by analyzing the phase of their communicated WiFi signals as the robots traverse the environment. This capability, based on the theory developed in our prior works, is made available for the first time as an opensource tool. It is motivated by the lack of easily deployable solutions that use robots' local resources (e.g WiFi) for sensing in NLOS. This has implications for localization, ad-hoc robot networks, and security in multi-robot teams, amongst others. The toolbox is designed for distributed and online deployment on robot platforms using commodity hardware and on-board sensors. We also release datasets demonstrating its performance in NLOS and line-of-sight (LOS) settings for a multi-robot localization usecase. Empirical results show that the bearing estimation from our toolbox achieves mean accuracy of 5.10 degrees. This leads to a median error of 0.5m and 0.9m for localization in LOS and NLOS settings respectively, in a hardware deployment in an indoor office environment.

Human review

How Pith checks papers → · Full audit of this paper →

How the literature uses it

3 arXiv papers cite this in 6 sentences

Each citing sentence is taken from the citing paper's LaTeX source and typed by Pith's classifier. A label appears at probability 0.8 or more (0.95 for support), a contest at 0.7 or more; the quoted words are the citing authors'. How each label was checked · JSON

Papers citing it · 1 sentence without a confident label

Uses its method or tool 1 paper

We process the AOA measurements using the WiFi sensing Toolbox from our earlier work [ref] .

Wi-Closure: Reliable and Efficient Search of Inter-robot Loop Closures Using Wireless Sensing · Hardware experiments · 1.00

Uses its data or values 1 paper

We use a standard deviation of $0.5 m^2$ for distance and $10$ for AOA, based on previous work characterizing these measurements [ref] .

Wi-Closure: Reliable and Efficient Search of Inter-robot Loop Closures Using Wireless Sensing · Simulation experiments · 0.90

Cites it as background 3 papers

Recently, [ref] also introduced sensing direction from the WiFi communication signal to the robotics community, requiring only a single WiFi antenna and movement of the robot.

Wi-Closure: Reliable and Efficient Search of Inter-robot Loop Closures Using Wireless Sensing · Related Work · 0.82
As an alternative to the RSSI metric, researchers have proposed the use of the Channel State Information (CSI) metric for robot localization systems [ref] .

Instantaneous Wireless Robotic Node Localization Using Collaborative Direction of Arrival · Related Work · 0.95
Past work has explored different methods for drone self-localization including vision, Lidar, IMUs, wireless, and sensor fusion [ref] .

3D Self-Localization of Drones using a Single Millimeter-Wave Anchor · Related Works · 1.00

Discussion

Know something about this paper? Leave a comment, or highlight any passage above to comment on it.