pith:W22OJFA5
Magnetic Indoor Localization through CNN Regression and Rotation Invariance
Rotation-invariant 2D magnetic features let a CNN regress accurate indoor positions without device orientation alignment.
arxiv:2604.22896 v2 · 2026-04-24 · cs.RO · cs.LG
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Claims
2D (Mn, Mg) inputs maintain rotation invariant accuracy and surpass the 3D inputs once rotation exceeds building-specific thresholds for three reference buildings: 0° for Loomis (large), 5° for Talbot (medium), and 6° for CSL (small).
That the chosen rotation-invariant features preserve enough position-specific information from the magnetic field and that the MagPie dataset trajectories represent realistic usage conditions without significant temporal or device variation.
Rotation-invariant magnetic features (norm and gravity projection) with a dilated CNN enable accurate, orientation-robust indoor position regression on the MagPie dataset.
Receipt and verification
| First computed | 2026-06-02T02:04:53.465456Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
b6b4e4941d432ef03286bd2bcc9378f9ec4cbb040c3b87aaa68842ead062fc33
Aliases
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Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/W22OJFA5IMXPAMUGXUV4ZE3Y7H \
| jq -c '.canonical_record' \
| python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: b6b4e4941d432ef03286bd2bcc9378f9ec4cbb040c3b87aaa68842ead062fc33
Canonical record JSON
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