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

Guangyi Liu, Wen Jiang, Boshu Lei, Vivek Pandey, Kostas Daniilidis, Nader Motee

TL;DR

The Risk-aware Environment Masking (RaEM) framework is proposed, which 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).

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.

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

TL;DR

The Risk-aware Environment Masking (RaEM) framework is proposed, which 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).

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.
Paper Structure (22 sections, 33 equations, 8 figures, 2 tables)

This paper contains 22 sections, 33 equations, 8 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Mathematical Notations
  3. Problem Formulation
  4. Technical Background
  5. Volumetric Rendering and Gaussian Splatting
  6. Measuring Risk of Collision
  7. Collision Risk Quantification
  8. Statistics of the Distance to 3D Gaussians
  9. Risk of Collision with the Uncertain Environment
  10. Risk-aware Environment Masking and the Risk-aware Next Best View
  11. Risk-Aware Environment Masking
  12. Next Best View Selection with RaEM
  13. Experiments
  14. Experiment Setup
  15. Implementation Details
  16. ...and 7 more sections

Figures (8)

  • Figure 1: The payload robot aiming to deliver the asset while the camera robot aims to find the NBV to improve the risk assessment.
  • Figure 2: Distance from waypoint $p$ to 3D Gaussians $x_i \in \mathcal{X}$.
  • Figure 3: Masking radius of RaEM.
  • Figure 4: Perceived map and uncertainty map among various scenes of Matterport3D. The predefined path is shown in aqua color, and a darker color in uncertainty map jiang2024fisherrf indicates less uncertainty. The result implies that RaEM helps the NBV algorithm to focus only on the safety-critical parts of the environment to reduce the reconstruction uncertainty and reveal more environments that are safety critical.
  • Figure 5: Uncertainty map comparison in the safety-critical region.
  • ...and 3 more figures