Scanning Bot: Efficient Scan Planning using Panoramic Cameras

2025-07-21

AI Summary

Existing panoramic RGB-D 3D reconstruction approaches suffer from manual viewpoint selection and platform navigation, difficulty in ensuring feature overlap across views, and high operational barriers for novice users. To address these challenges, this paper proposes a fully autonomous scanning planning framework that integrates panoramic RGB-D perception, viewpoint overlap optimization, topology-aware path planning, and real-time collision avoidance—enabling automatic generation of collision-free, high-coverage, low-redundancy inspection trajectories. Evaluated on complex indoor and outdoor scenes, the method achieves an average scan coverage of 99% and improves reconstruction efficiency by up to 3× over state-of-the-art methods. Its core innovation lies in the first joint modeling of panoramic visual geometric constraints and environmental topological structure, significantly enhancing usability for non-expert users and overall system robustness.

Technology Category

• Planning, Routing, and Scheduling: Replanning and Plan Repair
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Computer Vision: Vision for Robotics & Autonomous Driving

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs

Abstract

Panoramic RGB-D cameras are known for their ability to produce high quality 3D scene reconstructions. However, operating these cameras involves manually selecting viewpoints and physically transporting the camera, making the generation of a 3D model time consuming and tedious. Additionally, the process can be challenging for novice users due to spatial constraints, such as ensuring sufficient feature overlap between viewpoint frames. To address these challenges, we propose a fully autonomous scan planning that generates an efficient tour plan for environment scanning, ensuring collision-free navigation and adequate overlap between viewpoints within the plan. Extensive experiments conducted in both synthetic and real-world environments validate the performance of our planner against state-of-the-art view planners. In particular, our method achieved an average scan coverage of 99 percent in the real-world experiment, with our approach being up to 3 times faster than state-of-the-art planners in total scan time.

Problem

• Manual viewpoint selection slows 3D reconstruction
• Novice users struggle with spatial constraints
• Autonomous scan planning improves efficiency and coverage

Innovation

• Autonomous scan planning for panoramic cameras
• Collision-free navigation with viewpoint overlap
• Faster and more efficient than existing planners