Stereo and Mid-Level Vision Empower Dynamic Urban Navigation, Overcoming 1.5% Efficiency Limits of Monocular Foundation Models

Recent advances in foundation models and vision systems drive exploration into fully end-to-end robot navigation, but current approaches often bypass crucial mid-level vision processing, such as depth estimation and tracking, relying instead on direct mapping of visual input to movement. Wentao Zhou, Xuweiyi Chen, and Vignesh Rajagopal, all from the University of Virginia, alongside Jeffrey Chen, Rohan Chandra, and Zezhou Cheng, demonstrate that this approach is inefficient, particularly in complex, real-world environments. Their research introduces StereoWalker, a system that enhances foundation models with stereo vision and explicit mid-level processing to resolve depth ambiguity and improve understanding of dynamic scenes.

The team also created a new dataset of stereo navigation videos, automatically annotated for training and future research, and their experiments reveal that StereoWalker achieves state-of-the-art performance with significantly less training data, proving the value of integrating established vision techniques into modern robotic navigation systems.

Robot Navigation With Stereo Vision Tracking This research details StereoWalker, a visual navigation model for robots operating in urban environments, building upon previously published work. The study provides an in-depth look at the model’s architecture, implementation, and performance, alongside comprehensive ablation studies and deployment considerations. These investigations demonstrate the effectiveness of various components, including patch tokens, depth estimation, and tracking, in improving waypoint prediction accuracy across challenging scenarios such as turns, crossings, detours, and crowded environments. StereoWalker consistently outperforms comparable models in these tests, showcasing its enhanced navigational capabilities.

The team focused on improving urban visual navigation by incorporating stereo vision, depth estimation, and tracking to enhance waypoint prediction and overall robot navigation performance. The system leverages DINOv2 for image encoding, RT-MonSter++ and Depth-Anything-V2 for depth estimation, and CoTracker3 for point tracking, integrating these with tracking-guided attention layers. Stereo Vision and Foundation Models for Navigation This study pioneers a novel approach to robot navigation, termed StereoWalker, which integrates stereo vision and mid-level vision processing into a neural foundation model. Researchers addressed the limitations of relying solely on monocular vision by augmenting the system with stereo inputs, resolving depth-scale ambiguity and enhancing geometric understanding in dynamic scenes. To facilitate training and evaluation, the team curated a dedicated stereo navigation dataset, automatically annotating actions from existing internet stereo videos, enabling large-scale data expansion without manual intervention. StereoWalker’s core innovation lies in its architecture, which fuses information from multiple foundation vision models to create dense mid-level tokens encoding appearance, geometry, and motion. The system processes short temporal windows of rectified stereo frames, extracting high-level patch representations using DINOv2, estimating per-pixel depth with DepthAnythingV2, and generating point trajectories across time using CoTracker-v3. A key component is a depth aggregation module, compatible with both stereo and monocular inputs, which leverages a pretrained stereo matching network to refine disparity maps and calculate depth from stereo image pairs. These refined depth maps are then processed into depth embeddings, providing crucial geometric information. To maintain temporal correspondence and reduce drift, the team implemented a tracking-guided attention mechanism, processing tokens from all frames in the temporal window, followed by a global attention module to integrate scene context and a target-token attention mechanism to focus prediction on goal-relevant regions. The resulting architecture retains fine-grained spatial structure by preserving all patch tokens, unlike prior models that compress each frame, and supports both stereo and monocular inputs with the same framework. Experiments demonstrate that StereoWalker achieves comparable performance to state-of-the-art methods using only a small fraction of the training data and surpasses them with the full dataset, while also showing that stereo vision yields higher navigation performance than monocular input.

Stereo Vision Enables Robust Robot Navigation This research presents StereoWalker, a novel approach to robot navigation that integrates stereo vision and mid-level vision processing with fully end-to-end neural networks. Scientists demonstrate that relying solely on monocular vision and ignoring established vision techniques is inefficient for robust navigation, particularly in dynamic environments where precise geometric and motion understanding are crucial.

The team developed a system that leverages stereo inputs to resolve depth ambiguity and incorporates modern mid-level vision to provide reliable geometric and motion structure. Experiments using a standard navigation benchmark demonstrate that StereoWalker, after fine-tuning, achieves a mean prediction error of just over one meter, a small angular error, and a high arrival accuracy. These results represent significant improvements over existing methods, with reductions in prediction error and increases in arrival rates. Notably, the team also curated a new stereo navigation dataset with automatically annotated actions from internet stereo videos, facilitating training and future research. Further testing on a dedicated stereo benchmark reveals that StereoWalker, even without fine-tuning, outperforms monocular approaches, reducing average prediction error and increasing arrival rates. Real-world deployments confirm these findings, with StereoWalker achieving a high success rate in forward navigation and turns, demonstrating reliable performance in critical navigation scenarios.

The team observed that StereoWalker maintains a safer distance from pedestrians compared to existing methods, highlighting its potential for safe and efficient robotic navigation in complex environments.

Stereo Vision Boosts Robotic Navigation Efficiency This research demonstrates the significant benefits of integrating stereo vision and established mid-level vision techniques into end-to-end robotic navigation systems.

The team developed StereoWalker, a novel framework that combines stereo inputs with depth estimation and dense pixel tracking, addressing limitations inherent in systems relying solely on monocular vision. Results consistently show that StereoWalker achieves state-of-the-art performance on challenging urban navigation tasks, while requiring substantially less training data than existing methods. Specifically, the model attains comparable performance using only 1. 5% of the data needed by leading approaches, and surpasses them when trained with the full dataset. The work highlights the continued relevance of core computer vision principles in advancing robotic navigation, demonstrating that incorporating explicit geometric and motion understanding improves both efficiency and performance. While acknowledging that StereoWalker currently focuses on dynamic urban environments, the authors suggest that the principles explored could be extended to a broader range of robotic platforms and tasks. Future research will likely focus on applying these techniques to more diverse robotic systems and datasets, with the goal of achieving even greater generalization and flexibility in robotic navigation capabilities. 👉 More information 🗞 Empowering Dynamic Urban Navigation with Stereo and Mid-Level Vision 🧠 ArXiv: https://arxiv.org/abs/2512.10956 Tags: