Conflict-Aware Active Perception and Control in 3D Gaussian Splatting Fields via Control Barrier Functions

TL;DR

This paper presents a unified control framework for robots that balances safety and perception in uncertain 3D environments using Gaussian Splatting, Control Barrier Functions, and quadratic programming.

Contribution

It introduces a novel conflict-aware active perception and control method combining safety guarantees with information gain optimization in 3D Gaussian Splatting fields.

Findings

Improves safety and information acquisition over existing methods.

Uses a risk-aware Expected Information Gain for viewpoint selection.

Employs perception barrier functions for camera orientation alignment.

Abstract

Active perception in uncertain environments requires robots to navigate safely while acquiring informative observations to reduce map uncertainty. These objectives inherently conflict, as informative viewpoints often lie near uncertain regions with higher collision risk. To address this challenge, we develop a conflict-aware active perception and control framework for robotic systems operating in environments represented by 3D Gaussian Splatting (3DGS). Safety is enforced using a Control Barrier Function (CBF) derived from an Average Value-at-Risk AV@R collision-risk metric that accounts for geometric uncertainty and guarantees forward invariance of a safe set. To improve perception, we propose a risk-aware Expected Information Gain (EIG) formulation for selecting the next-best-view and introduce perception barrier functions that align the camera orientation with the local information-ascent direction. To obtain a tractable formulation for these conflicting safety and perception objectives, we propose a unified safety-critical, perception-aware quadratic program that enforces safety as a hard constraint while relaxing perception constraints through slack variables. Simulation results demonstrate that the proposed method improves both safety and information acquisition compared to existing 3DGS-based approaches.