OA-NBV: Occlusion-Aware Next-Best-View Planning for Human-Centered Active Perception on Mobile Robots

2026-03-10

AI Summary

This work addresses the challenge of incomplete visual perception of partially occluded individuals in cluttered environments, which significantly hinders human-centric tasks such as search and rescue. To overcome this limitation, the authors propose an occlusion-aware next-best-view (NBV) planning method that, for the first time, integrates a target-centered visibility model into NBV decision-making to explicitly optimize human body completeness in a single observation. The approach formulates a joint scoring mechanism incorporating target scale, occlusion status, and completeness to generate high-visibility candidate viewpoints while respecting robotic motion constraints, thereby closing the perception-planning loop. Experimental results demonstrate that the method achieves over 90% success rates in both simulated and real-world environments, yielding at least an 81% improvement in normalized target area and a minimum 58% increase in keypoint visibility compared to the strongest baseline.

Technology Category

• Humans and AI: Human-Aware Planning and Behavior Prediction
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Planning, Routing, and Scheduling: Activity and Plan Recognition

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs

Abstract

We naturally step sideways or lean to see around the obstacle when our view is blocked, and recover a more informative observation. Enabling robots to make the same kind of viewpoint choice is critical for human-centered operations, including search, triage, and disaster response, where cluttered environments and partial visibility frequently degrade downstream perception. However, many Next-Best-View (NBV) methods primarily optimize generic exploration or long-horizon coverage, and do not explicitly target the immediate goal of obtaining a single usable observation of a partially occluded person under real motion constraints. We present Occlusion-Aware Next-Best-View Planning for Human-Centered Active Perception on Mobile Robots (OA-NBV), an occlusion-aware NBV pipeline that autonomously selects the next traversable viewpoint to obtain a more complete view of an occluded human. OA-NBV integrates perception and motion planning by scoring candidate viewpoints using a target-centric visibility model that accounts for occlusion, target scale, and target completeness, while restricting candidates to feasible robot poses. OA-NBV achieves over 90% success rate in both simulation and real-world trials, while baseline NBV methods degrade sharply under occlusion. Beyond success rate, OA-NBV improves observation quality: compared to the strongest baseline, it increases normalized target area by at least 81% and keypoint visibility by at least 58% across settings, making it a drop-in view-selection module for diverse human-centered downstream tasks.

Problem

• Next-Best-View
• occlusion
• human-centered perception
• mobile robots
• active perception

Innovation

• Next-Best-View Planning
• Occlusion-Aware Perception
• Human-Centered Robotics
• Active Perception
• Mobile Robot Navigation