pith:E4PYRHYR
Safety-Critical LiDAR-Inertial Odometry with On-Manifold Deterministic Protection Level
LiDAR-inertial odometry supplies deterministic protection levels by turning bounded point-cloud noise into feasible pose sets through an on-manifold filter.
arxiv:2605.09383 v2 · 2026-05-10 · cs.RO
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Record completeness
Claims
By adopting the unknown but bounded assumption, we derive a neat closed-form relationship between point cloud noise and the uncertainty of the estimation from the iterated closest point algorithm. Using this relationship, we design an on-manifold ellipsoidal set-membership filter and implement it within the LIO system.
The unknown-but-bounded noise model for point clouds holds in real-world environments and the derived closed-form relationship accurately propagates uncertainty through ICP without hidden fitting steps or post-hoc exclusions.
A LiDAR-inertial odometry pipeline supplies deterministic feasible sets as protection levels by linking ICP point-cloud noise to pose uncertainty via a closed-form relation and propagating it with an on-manifold ellipsoidal set-membership filter.
Formal links
Receipt and verification
| First computed | 2026-06-19T16:12:54.782882Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
271f889f1136bf7f91c6ef0c807abd4274b55b2504081444a3c90fbc894b4945
Aliases
· · · · ·Agent API
Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/E4PYRHYRG27X7EOG54GIA6V5IJ \
| 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: 271f889f1136bf7f91c6ef0c807abd4274b55b2504081444a3c90fbc894b4945
Canonical record JSON
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