pith:J6PFFOJD
Environment-Adaptive Solid-State LiDAR-Inertial Odometry
Local normal-vector constraints and degeneracy-guided map updates stabilize solid-state LiDAR-inertial odometry in extreme environments.
arxiv:2604.15864 v2 · 2026-04-17 · cs.RO
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\pithnumber{J6PFFOJD5IJDLTT2ADPI4RUTFY}
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Record completeness
Claims
Experimental results demonstrate that the proposed method achieves superior mapping accuracy and robustness in extreme and perceptually degraded environments, with an average RMSE reduction of up to 12.8% compared to the baseline method.
That local normal-vector constraints will reliably suppress localization drift in degenerate scenarios and that the degeneration-guided map update strategy will improve map precision enough to enhance subsequent localization.
An adaptive LiDAR-inertial odometry method cuts localization RMSE by up to 12.8% in extreme environments via normal-vector constraints and smart map maintenance.
Receipt and verification
| First computed | 2026-05-29T01:05:09.993308Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
4f9e52b923ea1235ce7a00de8e46932e2ba59deeecd72a5e73e5a0f5fa60b5bb
Aliases
· · · · ·Agent API
Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/J6PFFOJD5IJDLTT2ADPI4RUTFY \
| 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: 4f9e52b923ea1235ce7a00de8e46932e2ba59deeecd72a5e73e5a0f5fa60b5bb
Canonical record JSON
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