Abstract: A system and method of semantic segmentation for an autonomous or semi-autonomous cleaning device. By using machine learning and visual data, one can teach an algorithm (e.g., a deep neural network) to distinguish between ground and non-ground spaces. The non-ground spaces are passed to the cleaning device path planning to avoid these areas. The algorithm may be programmed to detect low obstacles (e.g., cardboard boxes, low pallets, forklift tines) and other low-height obstacles. Semantic segmentation obstacle avoidance adds another layer of safety to perception safety protocols of a cleaning device.

Abstract: A system and method can be provided for detecting the status of one or more components and/or systems of, for example, a manual, semi-autonomous, or fully autonomous cleaning device or the like. Embodiments described herein relate to a system that provides semi-autonomous cleaning of surfaces by a semi-autonomous cleaning device. The system provides for improved reliable obstacle detection and avoidance, improved sensing, improved design, improved failure detection, advanced diagnostics and expandability capabilities.