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 of obstacle and cliff detection for an autonomous or a semi-autonomous cleaning device utilizing a calibration health monitor and occupancy grid filters. A calibration health monitor is used for monitoring and making minor adjustments to camera calibration over time. An occupancy grid filter is a 3D occupancy grid for probabilistically observing obstacles with 3d sensors that are susceptible to noise or other inaccuracies.

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.

Abstract: Systems and methods for electronically mapping a facility are presented. The method comprises obtaining a CAD file that includes graphical representations of a facility. An occupancy-map image is generated based on the CAD file. A sensor, such as a sensor on a self-driving vehicle, is used to detect a sensed feature within the facility. Based on the sensed feature, the occupancy-map image can be updated, since the sensed feature was not one of the known features in the CAD file prior to the sensed feature being detected by the sensor.

Abstract: Systems and methods for electronically mapping a facility are presented. The method comprises obtaining a CAD file that includes graphical representations of a facility. An occupancy-map image is generated based on the CAD file. A sensor, such as a sensor on a self-driving vehicle, is used to detect a sensed feature within the facility. Based on the sensed feature, the occupancy-map image can be updated, since the sensed feature was not one of the known features in the CAD file prior to the sensed feature being detected by the sensor.

Abstract: Systems and methods for electronically mapping a facility are presented. The method comprises obtaining a CAD file that includes graphical representations of a facility. An occupancy-map image is generated based on the CAD file. A sensor, such as a sensor on a self-driving vehicle, is used to detect a sensed feature within the facility. Based on the sensed feature, the occupancy-map image can be updated, since the sensed feature was not one of the known features in the CAD file prior to the sensed feature being detected by the sensor.

Abstract: Systems and methods for electronically mapping a facility are presented. The method comprises obtaining a CAD file that includes graphical representations of a facility. An occupancy-map image is generated based on the CAD file. A sensor, such as a sensor on a self-driving vehicle, is used to detect a sensed feature within the facility. Based on the sensed feature, the occupancy-map image can be updated, since the sensed feature was not one of the known features in the CAD file prior to the sensed feature being detected by the sensor.

Abstract: A system and/or 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. For example, systems and methods can be used for detecting degradation of back squeegee performance. In some embodiments, a system and/or method for detecting the status of one or more components is provided for detecting vacuum performance degradation by monitoring the current (amperage) being drawn by a vacuum motor. In addition, one or more other components can be monitored by inspecting images captured by a camera mounted on a back door of the cleaning device to determine, for example, one or more other problems associated with a squeegee mount, water pick up, and/or the like.

Abstract: According to one aspect, there are systems and methods for generating data indicative of a three-dimensional representation of a scene. Current depth data indicative of a scene is generated using a sensor. Salient features are detected within a depth frame associated with the depth data, and these salient features are matched with a saliency likelihoods distribution. The saliency likelihoods distribution represents the scene, and is generated from previously-detected salient features. The pose of the sensor is estimated based upon the matching of detected salient features, and this estimated pose is refined based upon a volumetric representation of the scene. The volumetric representation of the scene is updated based upon the current depth data and estimated pose. A saliency likelihoods distribution representation is updated based on the salient features. Image data indicative of the scene may also be generated and used along with depth data.