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 → 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).
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.
J. Mike Walker ’66 Department of Mechanical Engineering, Texas A&M University, College Station, TX 77840, USA · DEVCOM Army Research laboratory, Adelphi, MD, USA
Interdisciplinary Program in Artificial Intelligence, Seoul National University, Seoul, 08826, Korea. · Department of Electrical and Computer Engineering, Seoul National University, Seoul, 08826, Korea.