Splat-CBF: Safe Next-Best-View Control in 3D Gaussian-Splat Maps

2026-09-19

AI Summary

研究通过引入Splat-CBF方法，解决了机器人在未知环境中同时进行安全导航与有效感知的问题，结合碰撞避免和感知优化以提高导航效率。

Abstract

Where to look and how to move? A robot navigating an unmapped environment must do both at once, and the two goals pull against each other. The regions most worth observing are the ones the map knows least about, and those are exactly where the robot cannot trust its collision margins. We resolve this tension by introducing Splat-CBF, an active perception control barrier function that steers the camera toward the next best view while collision avoidance is enforced as a hard constraint. Safety is enforced by a risk-aware control barrier function that turns the Average Value-at-Risk of the Gaussian field into a single smooth hard constraint. Perception is enforced by a second barrier that rewards camera orientations with high expected Fisher information gain near the robot's planned path. The two meet in a quadratic program where safety is hard and perception is soft, with a slack penalty that adapts to how often perception has already been relaxed and how close the robot is to an uncertain region. We verify the method in indoor simulations, a Isaac Kinova manipulator and in experiments on an Ackermann-drive robot. Our results assert that robot navigates faster, gathers more information, and runs faster online than safety-only and perception-only baselines, giving up informative motion only when safety requires it.

Problem

• robot navigation
• unknown environment
• collision avoidance
• active perception
• Gaussian-Splat Maps

Innovation

• Splat-CBF
• Active Perception
• Control Barrier Function
• Risk-Aware
• Fisher Information Gain