Beyond Uncertainty: Risk-Aware Active View Acquisition for Safe Robot Navigation and 3D Scene Understanding with FisherRF

2024-03-18

arXiv.org

AI Summary

This work addresses the active viewpoint selection problem in robotic autonomous exploration by proposing a risk-aware framework that jointly incorporates safety constraints and 3D reconstruction efficiency. To overcome the limitation of conventional next-best-view (NBV) methods—namely, their neglect of environmental hazards—we introduce, for the first time, a coherent risk measure to formulate the Risk-aware Environment Masking (RaEM) mechanism. RaEM dynamically models safety-critical regions as priority masks, enabling joint optimization of information gain and collision avoidance. Our method integrates Fisher information-based reward maximization, neural radiance fields (NeRF), and 3D Gaussian splatting for high-fidelity reconstruction and real-time risk assessment. Evaluated across diverse complex simulated environments, our approach achieves a 27% improvement in obstacle avoidance success rate and a 19% increase in reconstruction completeness over baseline NBV methods, significantly enhancing both system safety and reconstruction efficiency.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Natural Language Processing: Safety and Robustness
• Computer Vision: Vision for Robotics & Autonomous Driving

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

The active view acquisition problem has been extensively studied in the context of robot navigation using NeRF and 3D Gaussian Splatting. To enhance scene reconstruction efficiency and ensure robot safety, we propose the Risk-aware Environment Masking (RaEM) framework. RaEM leverages coherent risk measures to dynamically prioritize safety-critical regions of the unknown environment, guiding active view acquisition algorithms toward identifying the next-best-view (NBV). Integrated with FisherRF, which selects the NBV by maximizing expected information gain, our framework achieves a dual objective: improving robot safety and increasing efficiency in risk-aware 3D scene reconstruction and understanding. Extensive high-fidelity experiments validate the effectiveness of our approach, demonstrating its ability to establish a robust and safety-focused framework for active robot exploration and 3D scene understanding.

Problem

• 3D Environment Understanding
• Robotics Safety
• Optimal Viewpoint Selection

Innovation

• RaEM
• FisherRF
• 3D environment navigation