Enabling the Next Generation of Smarter Self-navigating Robots using Hybrid ToF

White Paper Overview

The development of robot vacuum cleaners has evolved from using random, inefficient cleaning patterns to smart robots that generate maps of their environment for more efficient cleaning. This whitepaper discusses the design of slimmer robots with new features using time-of-flight cameras. Visual-SLAM (vSLAM) and SLAM technology are compared, with the latter utilizing depth-sensor time-of-flight (ToF) cameras to provide high-resolution 3D images. The REAL3™ hybrid Time-of-Flight (hToF) technology, developed by Infineon and partners, offers precise 3D point-cloud data, high-resolution depth data, and accurate close-range data, making it cost-efficient for consumer products such as cleaning robots. The hToF technology enables the design of slimmer robots and provides excellent map accuracies even in challenging environments. Additionally, the computational efficiency of hToF data processing and SLAM algorithms is evaluated on various embedded platforms used in robotics, showcasing its effectiveness and low computational load. In summary, the hToF technology with Infineon's REAL3™ sensors offers a powerful, cost-efficient solution for consumer robots, supporting SLAM, advanced obstacle avoidance, and cliff detection while reducing overall cost and system complexity.