3-D Relative Localization for Multi-Robot Systems with Angle and Self-Displacement Measurements

3-D Relative Localization for Multi-Robot Systems with Angle and Self-Displacement Measurements | Signal Canvas | ScienceToStartup

Page Freshness

Signal Canvas proof surface

Canonical route: /signal-canvas/3-d-relative-localization-for-multi-robot-systems-with-angle-and-self-displacement-measurements

stale

Proof freshness: stale
Proof status: unverified
Display score: 3/10
Last proof check: 2026-04-03
Score updated: 2026-04-03
Score fresh until: 2026-05-03
References: 0
Source count: 0
Coverage: 33%

This page is showing the last landed evidence receipt and score bundle because the latest proof data is outside the freshness window.

Agent Handoff

Canonical ID 3-d-relative-localization-for-multi-robot-systems-with-angle-and-self-displacement-measurements | Route /signal-canvas/3-d-relative-localization-for-multi-robot-systems-with-angle-and-self-displacement-measurements

REST example

curl https://sciencetostartup.com/api/v1/agent-handoff/signal-canvas/3-d-relative-localization-for-multi-robot-systems-with-angle-and-self-displacement-measurements

MCP example

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source_context

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Evidence Receipt

Route status: building

Claims: 8

References: Pending verification

Proof: Verification pending

Freshness state: computing

Source paper: 3-D Relative Localization for Multi-Robot Systems with Angle and Self-Displacement Measurements

PDF: https://arxiv.org/pdf/2604.01703v1

Source count: Pending verification

Coverage: 33%

Last proof check: 2026-04-03T20:50:40.820Z

Signal Canvas receipt window

Not build-ready: 3-D Relative Localization for Multi-Robot Systems with Angle and Self-Displacement Measurements

/buildability/3-d-relative-localization-for-multi-robot-systems-with-angle-and-self-displacement-measurements

Ignoreblocked

Subject: 3-D Relative Localization for Multi-Robot Systems with Angle and Self-Displacement Measurements

Verdict

Ignore

Verdict is Ignore because current viability and proof state do not clear the buildability gate.

Preparing verified analysis

GitHub Code Pulse

No public code linked for this paper yet.

Claim map

Strong 8Mixed 0Weak 0

Evidencepartial
in this paper we propose a novel and systematic 3-D relative localization framework based on inter-robot interior angle and self-displacement measurements
Implicationpartial
Explicitly stated in the abstract as the main contribution of the paper
Verificationpartial
partial
Evidencepartial
robots can determine their neighbors' relative positions and orientations in a purely linear manner
Implicationpartial
Directly stated in the abstract as a key result of the linear relative localization theory
Verificationpartial
partial
Evidencepartial
we present an advanced Maximum a Posterior (MAP) estimator by addressing three primary challenges existing in the MAP estimator
Implicationpartial
Explicitly listed in the abstract as the three challenges addressed by the MAP estimator
Verificationpartial
partial
Evidencepartial
we reformulate the linear computation process of the linear relative localization algorithm as a Weighted Total Least Squares (WTLS) optimization problem on manifolds
Implicationpartial
Directly stated in the abstract as a specific technical approach to address the non-convexity challenge
Verificationpartial
partial
Evidencepartial
The optimal solution of the WTLS problem is more accurate, which can then be used as initial values when solving the optimization problem associated with the MAP problem
Implicationpartial
Strongly implied in the abstract as the purpose of the WTLS reformulation
Verificationpartial
partial
Evidencepartial
we combine the WTLS with a Neural Density Estimator (NDE)
Implicationpartial
Directly stated in the abstract as a specific solution to the second challenge
Verificationpartial
partial
Evidencepartial
a marginalization mechanism is designed, which ensures that the computational cost remains constant
Implicationpartial
Directly stated in the abstract as the solution to the third challenge of increasing computational cost
Verificationpartial
partial
Evidencepartial
in order to deal with measurement noise, we present an advanced Maximum a Posterior (MAP) estimator
Implicationpartial
Implied from the motivation and the development of the MAP estimator to deal with noise
Verificationpartial
partial

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3-D Relative Localization for Multi-Robot Systems with Angle and Self-Displacement Measurements

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