Development and Adaptation of Robotic Vision in the Real-World: the Challenge of Door Detection

2024-01-31

Journal of Field Robotics

AI Summary

Service robots face significant challenges in real-time perception of door existence and passability within unstructured indoor environments, hindering reliable inference of dynamic topological maps. Method: This paper proposes a two-stage domain adaptation framework: (1) photorealistic synthetic data generation from robot-centric viewpoints, and (2) domain-specific qualification—a fine-tuning and robustness validation mechanism—to bridge the sim-to-real gap. Contribution/Results: The approach achieves high data efficiency and deployment reliability, overcoming the generalization limitations of generic vision models in long-horizon real-world scenarios. Extensive real-world evaluations across diverse indoor environments demonstrate that the method meets stringent requirements for long-term robotic deployment—achieving high door detection accuracy (>92%) and real-time inference speed (<50 ms per frame)—while significantly reducing domain adaptation cost. Moreover, it robustly supports higher-level tasks such as environmental topology tracking and update.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Computer Vision: Diffusion Models for Vision
• Machine Learning: Transfer, Domain Adaptation, Multi-Task Learning

Application Category

• Search and Retrieval-Augmented AI: Vertical and domain-specific search
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

Autonomous service robots are becoming increasingly common in human‐centric, long‐term deployments in unstructured indoor environments. Robotic vision is a crucial capability, enabling robots to perceive and interpret high‐level environmental features from visual input. While data‐driven approaches based on deep learning have advanced the capabilities of vision systems, applying these techniques in real robotic scenarios still presents unique methodological challenges. Conventional datasets often do not represent the object categories that a service robot needs to detect. More importantly, state‐of‐the‐art models struggle to address the demanding perception constraints faced by service robots, posing the need for adaptations to the specific environments in which the robots operate. We devise a method that addresses these challenges by leveraging photorealistic simulations to create synthetic visual datasets from a robot's perspective. This approach balances data quality with acquisition costs, enabling the training of deep, general‐purpose detectors tailored for service robots. We then demonstrate the benefits of qualifying a general detector for the domain in which the robot is deployed, studying the trade‐off between data‐acquisition efforts and performance improvement. We evaluate our method using a representative selection of prominent deep‐learning object detectors for the challenge of recognizing, in real time, the presence and traversability of doorways. This task, which we refer to as door detection, is fundamental to numerous significant robotic tasks, such as tracking the changing topology of dynamic environments. We conduct an extensive experimental campaign in the field, considering different real‐world setups while emulating the typical challenges encountered in long‐term deployments of service robots. Our key findings demonstrate that simulation and qualification techniques can significantly reduce costs associated with domain adaptation for service robots. While simulation allows embedding the robot's perspective during the training of end‐to‐end robotic vision modules, qualification is essential to improve their robustness over challenging detection instances, thus reaching the performance level typically required by realistic long‐term deployments of service robots.

Problem

• Developing robotic vision for real-world door detection tasks
• Adapting deep learning models to constrained robotic perception environments
• Balancing data acquisition costs with detection performance in robotics

Innovation

• Leveraging photorealistic simulations for robot vision training
• Adapting general detectors to specific deployment environments
• Balancing data acquisition costs with performance gains