Join us to discuss how Time-of-Flight (ToF) 3D depth sensing technology can be used to make robots smarter across applications such as obstacle avoidance, SLAM, path planning, and object manipulation. The session covers Infineon's ToF research and demonstrates real-world results, including autonomous navigation tests and map generation with accuracy as precise as 6 cm. A direct comparison with active stereo vision systems highlights ToF's key advantages in size, latency, sunlight performance, and depth data reliability, making it a strong choice for robotics applications.
Overview
This webinar explores how Time-of-Flight (ToF) 3D depth sensing technology can be used to develop smarter, more capable robots across a range of applications. The session covers the fundamentals of ToF imaging — including both direct and indirect sensing principles — and explains how depth cameras provide the critical third dimension that standard RGB cameras lack. Attendees will learn how this depth data enables key robotic capabilities such as obstacle avoidance, simultaneous localization and mapping (SLAM), path planning, object manipulation, and human interaction.
We'll highlight compact camera modules as small as 17 × 10 × 7 mm in both HQVGA and VGA resolutions. A particular focus is placed on the hybrid ToF module, which combines high-resolution flood illumination for detailed obstacle detection with long-range spot illumination for environment mapping — all in a single, miniaturized unit. Real-world demonstrations include a SLAM test run through a full office floor, achieving map accuracy as precise as 6 cm, and an autonomous obstacle avoidance run showing a robot successfully navigating a cluttered environment.
Key Takeaways
- Time-of-Flight cameras outperform active stereo systems in challenging conditions like bright sunlight and thin objects, while offering a significantly smaller form factor and lower computational demand.
- Depth data from ToF sensors is essential for enabling smarter robot behaviors — from autonomous navigation and path planning to object manipulation and natural human interaction.
- Infineon's hybrid ToF module combines high-resolution flood illumination for obstacle avoidance with long-range spot illumination for SLAM, achieving map accuracies as precise as 6 cm in real-world tests.
