SCOPE: Stochastic Cartographic Occupancy Prediction Engine for Uncertainty-Aware Dynamic Navigation

2024-06-28

arXiv.org

AI Summary

To address the challenge of predicting future robot states in complex dynamic environments, this paper proposes an uncertainty-aware stochastic occupancy prediction engine that jointly models robot ego-motion, dynamic object motion, and static scene geometry to generate multimodal distributions over future environmental states. Our method is the first lightweight, end-to-end framework for joint stochastic modeling of motion and geometry. Key innovations include a software-optimized stochastic occupancy map representation, a probabilistic propagation acceleration algorithm, and a unified training framework for heterogeneous multi-source data. Experimental results demonstrate significant efficiency improvements: 10× faster inference speed and 3× reduced memory footprint compared to prior approaches. On three real-world and simulated benchmarks, our method achieves superior prediction accuracy and robustness over state-of-the-art baselines, thereby enhancing the safety and reliability of downstream navigation policies.

Technology Category

• Intelligent Robots: State Estimation
• Reasoning under Uncertainty: Stochastic Optimization
• Planning, Routing, and Scheduling: Model-Based Reasoning

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

This article presents a family of Stochastic Cartographic Occupancy Prediction Engines (SCOPEs) that enable mobile robots to predict the future states of complex dynamic environments. They do this by accounting for the motion of the robot itself, the motion of dynamic objects, and the geometry of static objects in the scene, and they generate a range of possible future states of the environment. These prediction algorithms are software-optimized for real-time performance for navigation in crowded dynamic scenes, achieving 10 times faster inference speed and 3 times less memory usage than the original engines. Three simulated and real-world datasets collected by different robot models are used to demonstrate that these proposed prediction algorithms are able to achieve more accurate and robust stochastic prediction performance than other algorithms. Furthermore, a series of simulation and hardware navigation experiments demonstrate that the proposed predictive uncertainty-aware navigation framework with these stochastic prediction engines is able to improve the safe navigation performance of current state-of-the-art model- and learning-based control policies.

Problem

• Predict future states of dynamic environments for robots
• Optimize real-time navigation in crowded dynamic scenes
• Improve safe navigation with uncertainty-aware control policies

Innovation

• Predicts dynamic environments with stochastic modeling
• Optimized for real-time performance in navigation
• Improves safety with uncertainty-aware navigation framework