ViKiNG: Vision-Based Kilometer-Scale Navigation with Geographic Hints (2202.11271v3)

Abstract: Robotic navigation has been approached as a problem of 3D reconstruction and planning, as well as an end-to-end learning problem. However, long-range navigation requires both planning and reasoning about local traversability, as well as being able to utilize general knowledge about global geography, in the form of a roadmap, GPS, or other side information providing important cues. In this work, we propose an approach that integrates learning and planning, and can utilize side information such as schematic roadmaps, satellite maps and GPS coordinates as a planning heuristic, without relying on them being accurate. Our method, ViKiNG, incorporates a local traversability model, which looks at the robot's current camera observation and a potential subgoal to infer how easily that subgoal can be reached, as well as a heuristic model, which looks at overhead maps for hints and attempts to evaluate the appropriateness of these subgoals in order to reach the goal. These models are used by a heuristic planner to identify the best waypoint in order to reach the final destination. Our method performs no explicit geometric reconstruction, utilizing only a topological representation of the environment. Despite having never seen trajectories longer than 80 meters in its training dataset, ViKiNG can leverage its image-based learned controller and goal-directed heuristic to navigate to goals up to 3 kilometers away in previously unseen environments, and exhibit complex behaviors such as probing potential paths and backtracking when they are found to be non-viable. ViKiNG is also robust to unreliable maps and GPS, since the low-level controller ultimately makes decisions based on egocentric image observations, using maps only as planning heuristics. For videos of our experiments, please check out our project page https://sites.google.com/view/viking-release.

Overview of "ViKiNG: Vision-Based Kilometer-Scale Navigation with Geographic Hints"

The paper introduces ViKiNG, a navigation system that addresses the challenge of long-range robotic navigation using vision and geographic hints. Traditional methods often rely on 3D reconstruction for navigation, whereas ViKiNG innovatively combines learning-based control with heuristic planning based on geographic information like schematic roadmaps or satellite imagery.

Key Contributions

1. Hybrid Navigation Approach: ViKiNG elegantly integrates local traversability models with global planning heuristics. This dual approach allows it to navigate uncharted environments without explicit geometric reconstruction. Instead, ViKiNG utilizes a topological environment representation, maintaining robustness against noise in map data.
2. Latent Goal Model: The system employs a latent goal model, which predicts temporal distances and actions based on current observations. By leveraging a large dataset, ViKiNG can effectively evaluate and select subgoals, thereby facilitating navigation through previously unseen terrain.
3. Geographic Hints as Heuristics: Geographic side information, even if inaccurate, is used as a heuristic rather than a control input. This enables the robot to employ global planning strategies while making egocentric image-based decisions for local control.
4. Physical Search Algorithm (): The paper introduces a novel A$^*$-like algorithm for physical search, accommodating the challenges posed by real-world navigation, such as the inability to pre-evaluate paths without prior traversal.

Experimental Results

The paper evaluates ViKiNG through a series of experiments across diverse environments, including suburban, nature parks, and university campuses. It successfully navigates paths exceeding 2 kilometers—a notable achievement, considering its training was limited to trajectories under 80 meters.

1. Operational Efficiency: In comparison to baselines including BC, PPO, GCG, and RECON-H, ViKiNG showed superior performance, achieving a 100% success rate in trials across varying goal distances and environmental complexities.
2. Robustness to Inaccurate Maps: ViKiNG’s reliance on learned control means it remains capable even when faced with outdated or erroneous map data, maintaining effective pathfinding.
3. Comparison of Geographic Hints: ViKiNG's approach to learning geographic hints from different sources, like roadmaps versus satellite images, emphasizes its flexibility in identifying optimal paths through environmental variations.

Implications and Future Developments

The paper implies significant advancements in autonomous navigation systems. By combining learned models with heuristic-based planning, ViKiNG aligns with human-like navigation strategies, utilizing both local observations and broad environmental knowledge. Future developments could explore incorporating additional data types, such as textual directions or traditional maps, thus broadening ViKiNG’s applicability. Furthermore, extending the learned models to encompass higher-level semantic understanding could enhance decision-making processes in complex urban scenarios.

ViKiNG represents a notable stride in robotic navigation, showcasing how machine learning can extend the capabilities of autonomous systems in traversing large-scale distances with limited preparatory data. As AI and robotics continue to integrate, systems like ViKiNG could redefine operational paradigms across various industries, emphasizing the role of machine-learned model integration in practical applications.