Pith Number

pith:R6MQHHIP

pith:2026:R6MQHHIPISJL3JC64JHR7HTPBR

not attested not anchored not stored refs pending

DynoSLAM: Dynamic SLAM with Generative Graph Neural Networks for Real-World Social Navigation

Danil Tokhchukov, Gonzalo Ferrer, Veronika Morozova

DynoSLAM uses Monte Carlo rollouts from graph neural networks to capture pedestrian uncertainty and embed it into SLAM optimization.

arxiv:2605.02759 v2 · 2026-05-04 · cs.RO · cs.CV

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\usepackage{pith}
\pithnumber{R6MQHHIPISJL3JC64JHR7HTPBR}

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Record completeness

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2 Internet Archive

3 Author claim open · sign in to claim

4 Citations open

5 Replications open

✓ Portable graph bundle live · download bundle · merged state

The bundle contains the canonical record plus signed events. A mirror can host it anywhere and recompute the same current state with the deterministic merge algorithm.

Claims

C1strongest claim

By utilizing Monte Carlo rollouts from a trained GNN, we capture the multimodal epistemic uncertainty of human interactions and embed it into the SLAM graph via a dynamic Mahalanobis distance factor.

C2weakest assumption

That Monte Carlo samples from the pre-trained GNN will produce uncertainty estimates that integrate stably into the factor-graph optimizer without introducing new failure modes or requiring post-hoc tuning.

C3one line summary

DynoSLAM embeds stochastic pedestrian motion predictions from generative GNNs into a dynamic GraphSLAM factor graph to enable safer robot navigation in human environments.

Receipt and verification

First computed	2026-05-20T00:03:13.762028Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

8f99039d0f4492bda45ee24f1f9e6f0c4a361f678398a211d2dadfc5c0bfbcf2

Aliases

arxiv: 2605.02759 · arxiv_version: 2605.02759v2 · doi: 10.48550/arxiv.2605.02759 · pith_short_12: R6MQHHIPISJL · pith_short_16: R6MQHHIPISJL3JC6 · pith_short_8: R6MQHHIP

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Resolver JSON Graph JSON Events JSON Schema Signing key

Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/R6MQHHIPISJL3JC64JHR7HTPBR \
  | 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: 8f99039d0f4492bda45ee24f1f9e6f0c4a361f678398a211d2dadfc5c0bfbcf2

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "8b0a5f494dedf0ba93776d167f0efae9abc808abafcc9651e2bcae4715194169",
    "cross_cats_sorted": [
      "cs.CV"
    ],
    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "primary_cat": "cs.RO",
    "submitted_at": "2026-05-04T15:58:08Z",
    "title_canon_sha256": "7e3dc8a35b453715b51bda6a4918e6228c68093c8147a2c946761a861f08f745"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2605.02759",
    "kind": "arxiv",
    "version": 2
  }
}