FLINT: Fast Lightweight Inference for Traversability

William Bonilla; Maxime Boisvert; David-Alexandre Poissant; David Meger; Louis Petit

cs.ROSep 22, 2026

FLINT: Fast Lightweight Inference for Traversability

Authors: William Bonilla, Maxime Boisvert, David-Alexandre Poissant, David Meger, Louis Petit

Organizations: School of Computer Science, McGill University, Montreal, QC, Canada · Centre de Technologies Avanc´ees (CTA), Sherbrooke, QC, Canada · Universit´e de Sherbrooke, Sherbrooke, QC, Canada

Abstract

Navigation in off-road conditions is challenging due to the lack of structure. There is no fixed vocabulary for what is traversable. The traversability depends on both the environment and the embodiment's dynamics. Neither of these two variables can be hand-labeled at scale. Thus, traversability has to be learned by the embodiment's own experience. Modern platforms tend to use multiple sensors to estimate traversability and navigate: RGBD cameras, lidar, radar, IMU, with computationally intensive platforms to run inference on neural networks. Against this trend, we propose FLINT, a lightweight traversability estimator: a 21.6M-parameter backbone, 38\times smaller than a comparable foundation-model backbone, that scores higher on held-out terrain probes and runs at 14.7 FPS on CPU alone using a RGB camera has the only sensor. Despite that gap in scale, FLINT produces a cheaper, more accurate costmap than a deployed foundation-model system (WildOS) on 23 of 24 replayed field logs. We compare different self-supervised learning signals and deploy the resulting models on a real platform in closed-loop field trials: the best self-supervised head reaches 99% autonomy over the route, outperforming a human-label-trained baseline deployed live on the same course. Our results show that heavy sensing and computing are not necessary for traversability estimation.

Figures & tables

Fig. 1: Label generation on one TartanDrive 2.0 frame (open grass field, figure_8_morning run, frame 003058). (a) RGB input; (b) DINOv3 patch features (PCA $\to$ RGB); (c) k-means clusters (k=20) on those features. The three self-supervision signals: (d) traj — reprojected 2 s wheel corridor (green) with sampled negatives (red points) outside a dilated margin; (e) sam3 — SAM3 instances classified by corridor overlap (green positive, red confident-negative sky/tree, unshaded abstain), which propagates the narrow corridor to the entire drivable surface; (f) stego — clusters labeled by corridor overlap on the patch grid. The same drive supervises each head very differently: the corridor band alone (d), the full coherent surface (e), or feature-space clusters (f).

Panel of Figure 2 — Fig. 2: MLP head architecture: a 384-d frozen DINOv3 patch feature is mapped through a 512-unit hidden layer to a scalar traversability score.

Fig. 3: System Overview. A single RGB frame captured by the Husky A200’s Multisense S27 stereo camera is processed by a frozen DINOv3 (ViT-S/16) backbone into patch-level features. Four lightweight MLP heads (HLTM, SAM3, STEGO, TRAJ) independently propagate these features into a dense traversability probability map, shown here for each head on real field data (blue = high traversability probability, orange = low). The active head’s probabilities are passed through an inverse perspective mapping (IPM) module to produce a ground-plane occupancy grid, which Nav2 consumes as a BEV costmap to generate a planned trajectory (white corridor) via its MPPI/A*/arc planner. Colored regions denote organizational groupings only (hardware, FLINT, Nav2), not a claim of novelty.

System	Mean pct.	$<$ WildOS	ms/frame	GPU MB
stego	18.1	23/24	70.6	391
traj	23.8	23/24	70.6	391
sam3	26.5	22/24	70.6	391
WildOS [ 7 ]	41.0	—	405.3	1045
WVN (shadow) [ 4 , 5 ]	54.2	4/24	44.0	382

TABLE I: Offline comparison against foundation-model baselines

Fig. 4: GPS trajectories for stego, sam3 and HLTM heads on the identical route and planner, overlaid on a shared basemap. Clustered operator-intervention episodes are marked per head. Added below are example interventions: successful navigation, a minor intervention near the trail edge, and a major intervention into high grass.

model	superv.	AUROC	AUPRC	IoU
traj	self, geom.	0.639	0.447	0.366
stego	self, clust.	0.948	0.911	0.418
sam3	self, SAM3	0.956	0.918	0.749
HLTM	human	0.985	0.963	0.824

TABLE II: Zero-shot RELLIS-3D evaluation (IoU at fixed threshold

\tau=0.5

Head	Dist. (m)	Auton. %	Longest (m)	Longest (s)	Interv.
sam3	1125.7	99.0	398.3	639.8	3
stego	1059.7	97.7	809.9	1313.4	3
HLTM	1114.5	96.3	279.8	454.8	7
traj	1165.2	87.5	197.0	315.8	8

TABLE III: Self-supervised heads vs. the human-labeled baseline, identical route and planner.

Fig. 5: 3D reconstruction of the route’s sharpest turn, colored by head, with satellite insent showing the driven path

Robot Traversability Estimation

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Jul 26, 2026

Learning Traversability for Long Horizon Off-Road Navigation

Autonomous navigation across large off-road environments remains a challenging problem. Onboard sensors perceive only the immediate surroundings, yet safe and efficient routes depend on terrain features that extend well beyond the sensor horizon. Geo-spatial data sources such as satellite imagery, aerial LiDAR, and vector maps can close this gap, but learning traversability from them is difficult: dense labels are unavailable at scale, and existing methods rely on short-range sensing. We propose an efficient formulation that learns a continuous traversability map from overhead data, supervised directly by human-driven GPS trajectories and shaped by supervised geometric priors from LiDAR. Alongside the model, we release a dataset, curated from public sources, consisting of 299 scenes spanning

\sim\!1{,}244\,\mathrm{km}^{2}

of diverse terrain, paired with

1{,}130\,\mathrm{km}

of human driving. In field trials on a Clearpath Warthog across seven routes at two sites,our method achieves trajectories within

5.5\%

of human path length and reduces operator interventions by

\sim\!85\%

compared to local-planner-only autonomy.

Kasi Viswanath, Jason M. Gregory, Shaunak Kolhe +1

J. Mike Walker ’66 Department of Mechanical Engineering, Texas A&M University, College Station, TX 77840, USA · DEVCOM Army Research laboratory, Adelphi, MD, USA

Off-Road Robot Navigation Geospatial Representation Learning Robot Navigation Safe Robot Navigation Robot Traversability Estimation

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Jul 20, 2026

Distilling Global Traversability Priors for Image-based Affordance Prediction in Off-road Environments

Standard methods for autonomous navigation in unstructured terrain are prone to myopic behaviors in long-horizon scenarios. The use of metric maps built from LiDAR or cameras provides necessary local geometry and semantic information but is strictly limited by depth sensing range. By discarding data beyond the mapping horizon robots suffer from suboptimal, short-sighted decisions. To recover this lost information, we focus on extracting long-range traversability-aware frontiers directly from first-person-view (FPV) images. By leveraging satellite imagery, we compute the set of feasible navigation paths for a dataset of image/pose pairs and use them to supervise our network, reducing the need for extensive human demonstration data. We demonstrate that this approach improves performance in long-range off-road navigation over existing methods by more than 10% in various offline benchmarks and reduces the number of human interventions incurred in a set of real-world experiments. More details can be found at https://theairlab.org/ss_frontiers_iros .

Matthew Sivaprakasam, Samuel Triest, Micah Nye +5

Robotics Institute, Carnegie Mellon University · Field AI

Off-Road Robot Navigation Embodied Navigation Robot Navigation Robot Traversability Estimation

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Mar 10, 2026

STONE Dataset: A Scalable Multi-Modal Surround-View 3D Traversability Dataset for Off-Road Robot Navigation

Reliable off-road navigation requires accurate estimation of traversable regions and robust perception under diverse terrain and sensing conditions. However, existing datasets lack both scalability and multi-modality, which limits progress in 3D traversability prediction. In this work, we introduce STONE, a large-scale multi-modal dataset for off-road navigation. STONE provides (1) trajectory-guided 3D traversability maps generated by a fully automated, annotation-free pipeline, and (2) comprehensive surround-view sensing with synchronized 128-channel LiDAR, six RGB cameras, and three 4D imaging radars. The dataset covers a wide range of environments and conditions, including day and night, grasslands, farmlands, construction sites, and lakes. Our auto-labeling pipeline reconstructs dense terrain surfaces from LiDAR scans, extracts geometric attributes such as slope, elevation, and roughness, and assigns traversability labels beyond the robot's trajectory using a Mahalanobis-distance-based criterion. This design enables scalable, geometry-aware ground-truth construction without manual annotation. Finally, we establish a benchmark for voxel-level 3D traversability prediction and provide strong baselines under both single-modal and multi-modal settings. STONE is available at: https://konyul.github.io/STONE-dataset/

Konyul Park, Daehun Kim, Jiyong Oh +7

Interdisciplinary Program in Artificial Intelligence, Seoul National University, Seoul, 08826, Korea. · Department of Electrical and Computer Engineering, Seoul National University, Seoul, 08826, Korea.

Off-Road Robot Navigation Robot Traversability Estimation 3D Occupancy Prediction

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Abstract

Robot Traversability Estimation

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