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

• URL: http://arxiv.org/abs/2607.17984v1
• Date: Mon, 20 Jul 2026 14:16:17 GMT
• Title: Distilling Global Traversability Priors for Image-based Affordance Prediction in Off-road Environments
• Abstract summary: Standard methods for autonomous navigation in unstructured terrain are prone to myopic behaviors in long-horizon scenarios.<n>We focus on extracting long-range traversability-aware frontiers directly from first-person-view (FPV) images.<n>We demonstrate that this approach improves performance in long-range off-road navigation over existing methods by more than 10% in various offline benchmarks.
• Score: 11.788562558317288
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 .

Related papers

• Spatial Retrieval Augmented Autonomous Driving [81.39665750557526]
  Existing autonomous driving systems rely on onboard sensors for environmental perception.<n>We propose the spatial retrieval paradigm, introducing offline retrieved geographic images as an additional input.<n>We will open-source dataset curation code, data, and benchmarks for further study of this new autonomous driving paradigm.
  arXiv  Detail & Related papers  (2025-12-07T14:40:49Z)
• FOM-Nav: Frontier-Object Maps for Object Goal Navigation [65.76906445210112]
  FOM-Nav is a framework that enhances exploration efficiency through Frontier-Object Maps and vision-language models.<n>To train FOM-Nav, we automatically construct large-scale navigation datasets from real-world scanned environments.<n> FOM-Nav achieves state-of-the-art performance on the MP3D and HM3D benchmarks, particularly in navigation efficiency metric SPL.
  arXiv  Detail & Related papers  (2025-11-30T18:16:09Z)
• SuperEx: Enhancing Indoor Mapping and Exploration using Non-Line-of-Sight Perception [4.882290104560518]
  In this work, we bring non-line-of-sight (NLOS) sensing to robotic exploration.<n>We leverage single-photon LiDARs, which capture time-of-flight histograms that encode the presence of hidden objects.<n>We introduce SuperEx, a framework that integrates NLOS sensing directly into the mapping-exploration loop.
  arXiv  Detail & Related papers  (2025-10-12T08:52:20Z)
• CREStE: Scalable Mapless Navigation with Internet Scale Priors and Counterfactual Guidance [13.922655150502365]
  CREStE is a scalable learning-based mapless navigation framework.<n>It addresses the open-world generalization and robustness challenges of outdoor urban navigation.<n>We evaluate CREStE on the task of kilometer-scale mapless navigation in a variety of city, offroad, and residential environments.
  arXiv  Detail & Related papers  (2025-03-05T21:42:46Z)
• FrontierNet: Learning Visual Cues to Explore [54.8265603996238]
  This work aims at leveraging 2D visual cues for efficient autonomous exploration, addressing the limitations of extracting goal poses from a 3D map.<n>We propose a visual-only frontier-based exploration system, with FrontierNet as its core component.<n>Our approach provides an alternative to existing 3D-dependent goal-extraction approaches, achieving a 15% improvement in early-stage exploration efficiency.
  arXiv  Detail & Related papers  (2025-01-08T16:25:32Z)
• Pixel to Elevation: Learning to Predict Elevation Maps at Long Range using Images for Autonomous Offroad Navigation [10.898724668444125]
  We present a learning-based approach capable of predicting terrain elevation maps at long-range using only onboard egocentric images in real-time. We experimentally validate the applicability of our proposed approach for autonomous offroad robotic navigation in complex and unstructured terrain.
  arXiv  Detail & Related papers  (2024-01-30T22:37:24Z)
• OccNeRF: Advancing 3D Occupancy Prediction in LiDAR-Free Environments [77.0399450848749]
  We propose an OccNeRF method for training occupancy networks without 3D supervision. We parameterize the reconstructed occupancy fields and reorganize the sampling strategy to align with the cameras' infinite perceptive range. For semantic occupancy prediction, we design several strategies to polish the prompts and filter the outputs of a pretrained open-vocabulary 2D segmentation model.
  arXiv  Detail & Related papers  (2023-12-14T18:58:52Z)
• URA*: Uncertainty-aware Path Planning using Image-based Aerial-to-Ground Traversability Estimation for Off-road Environments [4.826948318242962]
  This research proposes an uncertainty-aware path planning method, URA* using aerial images for autonomous navigation in off-road environments. The proposed planner also incorporates replanning techniques to allow rapid replanning during online robot operation. Results show that the proposed image segmentation and planning methods outperform conventional planning algorithms in terms of the quality and feasibility of the initial path.
  arXiv  Detail & Related papers  (2023-09-15T23:52:45Z)
• Monocular BEV Perception of Road Scenes via Front-to-Top View Projection [57.19891435386843]
  We present a novel framework that reconstructs a local map formed by road layout and vehicle occupancy in the bird's-eye view. Our model runs at 25 FPS on a single GPU, which is efficient and applicable for real-time panorama HD map reconstruction.
  arXiv  Detail & Related papers  (2022-11-15T13:52:41Z)
• Cross-Camera Trajectories Help Person Retrieval in a Camera Network [124.65912458467643]
  Existing methods often rely on purely visual matching or consider temporal constraints but ignore the spatial information of the camera network. We propose a pedestrian retrieval framework based on cross-camera generation, which integrates both temporal and spatial information. To verify the effectiveness of our method, we construct the first cross-camera pedestrian trajectory dataset.
  arXiv  Detail & Related papers  (2022-04-27T13:10:48Z)
• Adaptive Path Planning for UAVs for Multi-Resolution Semantic Segmentation [28.104584236205405]
  A key challenge is planning missions to maximize the value of acquired data in large environments. This is, for example, relevant for monitoring agricultural fields. We propose an online planning algorithm which adapts the UAV paths to obtain high-resolution semantic segmentations.
  arXiv  Detail & Related papers  (2022-03-03T11:03:28Z)
• Lifelong Topological Visual Navigation [16.41858724205884]
  We propose a learning-based visual navigation method with graph update strategies that improve lifelong navigation performance over time. We take inspiration from sampling-based planning algorithms to build image-based topological graphs, resulting in sparser graphs yet with higher navigation performance compared to baseline methods. Unlike controllers that learn from fixed training environments, we show that our model can be finetuned using a relatively small dataset from the real-world environment where the robot is deployed.
  arXiv  Detail & Related papers  (2021-10-16T06:16:14Z)
• Occupancy Anticipation for Efficient Exploration and Navigation [97.17517060585875]
  We propose occupancy anticipation, where the agent uses its egocentric RGB-D observations to infer the occupancy state beyond the visible regions. By exploiting context in both the egocentric views and top-down maps our model successfully anticipates a broader map of the environment. Our approach is the winning entry in the 2020 Habitat PointNav Challenge.
  arXiv  Detail & Related papers  (2020-08-21T03:16:51Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.