cs.ROOct 22, 2026

A Sparse Topometric Graph for Scalable Cross-Session Indoor Navigation

Authors: Eun Sun Lee, Minkyun Seo, Gwangtak Bae, Junho Kim, Seunggu Kang, Jaesik Park, Young Min Kim

Organizations: Seoul National University

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Abstract

A conventional embodied agent in an indoor environment builds a map by accumulating noisy real-world measurements and results from heavy inference to guide navigation. While the detailed measurements may differ due to scene changes or sensor settings, the unique topological structure of navigable passages or the connectivity between rooms may provide training-free guidance that complements local observations. We propose a sparse topometric graph as an efficient structural representation for robust indoor navigation, alleviating the need for fine-grained matching, which is computationally expensive, memory-intensive, and prone to real-world variations. The map consists of nodes that encode local 3D geometry and distance-weighted edges that capture traversable connectivity. A learned graph-matching network finds consistent subgraph structures across sessions by considering both node-level geometric descriptors and neighborhood connectivity. The holistic contextual matching is robust to local ambiguities caused by weak distinctiveness or repetitive structure, and establishes stable correspondences despite local scene changes, different sensor modalities, or limited spatial overlap. We analyze the practicality of the graph through cross-session point-goal navigation, where a goal specified in a prior reference map must be reached from an unknown initial pose in a later session. Experiments show that our method achieves the highest success rates (77.1% on Gibson and 40.0% under 3D-FRONT scene changes), while reducing memory by more than two orders of magnitude compared to point-level representations. The proposed approach further supports cross-session alignment and goal-directed navigation with strong scalability and robust real-world transfer.

Figures & tables

Figure 2: Overview of the proposed cross-session localization and navigation framework. Given depth DtD_{t} and odometry OtO_{t} inputs, the agent constructs a topometric graph using medial-axis decomposition and assigns geometric descriptors from local point clouds. Node correspondences MM are estimated across reference graph Gref\mathcal{G}^{\text{ref}} and current graph Gcur\mathcal{G}^{\text{cur}} via graph matching. The correspondences are reused for both localization and navigation. The navigation policy combines the goal-directed score SgoalS_{\text{goal}} with the frontier-based exploration score SexploreS_{\text{explore}} to select a node as the next waypoint toward the goal position gg of the cross-session navigation task.
Figure 3: Training pair construction for graph matching. (a) Topometric graphs are extracted from two sessions with partially overlapping coverage of the same scene. (b) Ground-truth node correspondences are established by pairwise Euclidean distances between node centers across sessions. (c) Gaussian noise is injected into the edge weight to simulate odometry drift and construct training graph pairs.
DatasetMax StepsGoal Thr. (m)Avg. Area (m 2 )
3D-FRONT5001.080.6
Gibson5002.083.4
HM3D10002.0212.8
Table 1: Evaluation protocol across datasets.
 NoiselessNoisy ( σ=1.0\sigma=1.0 )
MethodSRSPLSRSPL
ANS [ 6 ]40.0033.4040.0033.13
RnR [ 15 ]30.0023.6741.4132.38
AnyLoc [ 14 ]40.0010.9130.005.69
ObjReact [ 10 ]54.2840.8154.2840.81
ConceptGraph [ 11 ]41.4322.9332.8616.40
Table 2: Cross-session point-goal navigation results on Gibson [ 34 ] under noiseless and noisy odometry.
Figure 4: Navigation performance in large-scale HM3D environments [ 22 ] . Cumulative number of successful episodes out of 100 as the geodesic separation between the reference- and current-session start positions increases.
Figure 5: Qualitative cross-session navigation results. (a–b) Navigation examples in HM3D and 2D-3D-S environments, respectively. (c–d) Examples under scene changes in Gibson and 3D-FRONT, where modified regions are highlighted by red boxes. Across these settings, the proposed topometric graph supports cross-session localization and goal-directed navigation despite partial overlap and local environmental changes.
 ANSRnRAnyLocObjReactConceptGraphOurs
SR13.3313.3320.006.6713.3340.00
SPL8.368.836.916.674.9220.18
Table 3: Cross-session point-goal navigation results under scene changes on 3D-FRONT [ 9 ] .
MethodScene RepresentationStorage (MB)
ANS [ 6 ]Grid Map1.76
RnR-Map [ 15 ]Feature Map2.00
AnyLoc [ 14 ]Place Descriptor Map13.25
ObjReact [ 10 ]Object Graph0.80
ConceptGraph [ 11 ]Scene Graph with Language Features5.00
BUFFER-X [ 28 ]Point Cloud2.81
Table 4: Memory comparison across methods on Gibson [ 34 ] .
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