Time-to-Collision Based Dynamic Obstacle Avoidance Using Pretrained Vision Models for Robots in Unstructured Environments

2026-07-11 · Source: cs.AI updates on arXiv.org · Field: Technology & Digital — Robotics & Autonomous Systems, Artificial Intelligence & Machine Learning · Depth: Expert, extended

Summary

A novel method for vision-based dynamic obstacle avoidance for autonomous mobile robots in unstructured environments is introduced. This data-efficient and interpretable approach utilizes UniDepth, a pretrained monocular depth estimation model, to generate dense depth maps from RGB video. It extends the SuperPoint and SuperGlue feature correspondence pipeline to track keypoints, projecting them into 3D using camera intrinsics and predicted depth. Bundle adjustment, initialized from these 3D keypoints, computes per-keypoint time-to-collision (TTC), guiding a 2D motion primitive to evade the closest obstacle. Evaluated on the M3ED dataset, the pipeline achieved a precision of 0.49 and recall of 0.38 for identifying frames with TTC below 1 second. It detected at least one frame with TTC < 1 second for 20 out of 22 unique physical obstacles and generated correct evasive motion in 84% of true positive detections. This method eliminates model training, requiring only 74 seconds of data for hyperparameter tuning.

Key takeaway

For robotics engineers developing autonomous navigation systems in complex outdoor environments, this approach offers a data-efficient alternative to simulation-trained policies. You should consider integrating pretrained vision models like UniDepth, SuperPoint, and SuperGlue to achieve interpretable dynamic obstacle avoidance. Be aware that current recall for individual frame detection is 0.38, so robust long-term tracking is critical for reliable deployment. Further work on integrating motion primitives with robot locomotion controllers is needed.

Key insights

Pretrained vision models enable data-efficient, interpretable dynamic obstacle avoidance for robots in unstructured environments.

Principles

• Pretrained vision models bypass the sim-to-real transfer problem.
• Geometric TTC computation offers transparent interpretability.
• Long-term keypoint tracking is crucial for robust obstacle avoidance.

Method

The method involves UniDepth for monocular depth, SuperPoint/SuperGlue for keypoint tracking, 3D projection, XM solver for bundle adjustment, per-keypoint TTC computation, and 2D motion primitive generation.

In practice

• Integrate UniDepth for dense monocular depth estimation.
• Apply SuperPoint and SuperGlue for robust keypoint tracking.
• Filter 3D keypoints by depth and displacement for accuracy.

Topics

• Dynamic Obstacle Avoidance
• Monocular Depth Estimation
• UniDepth
• SuperPoint & SuperGlue
• Robotics Perception
• Sim-to-Real Transfer

Best for: Research Scientist, Robotics Engineer, AI Scientist, Computer Vision Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.AI updates on arXiv.org.