Mcneilus Truck and Manufacturing, Inc.

Waterloo Controls Inc.

Kenneth Alexander McIsaac

Leo Peter Van Kampen

Justin Szoke-Sieswerda

Brandon Castellano

Systems and methods for detecting a waste receptacle, the system including a camera for capturing an image, a convolutional neural network, and processor. The convolutional neural network can be trained for identifying target waste receptacles. The processor can be mounted on the waste-collection vehicle and in communication with the camera and the convolutional neural network configured for using the convolutional neural network. The processor can be configured for using the convolutional neural network to generate an object candidate based on the image; using the convolutional neural network to determine whether the object candidate corresponds to a target waste receptacle; and selecting an action based on whether the object candidate is acceptable.

Systems and methods for detecting waste receptacles using convolutional neural networks

Systems and methods for detecting and picking up a waste receptacle, the system being mountable on a waste-collection vehicle, and including an arm for grasping the waste receptacle, a processor, a camera, a non-transitory computer-readable medium, and an arm-actuation module. The processor is configured for generating a pose candidate based on an image captured by the camera, verifying that the pose candidate matches a template representation stored on the medium, and calculating a location of the waste receptacle. The arm-actuation module can be configured to automatically move the arm in response to the calculated location, in order to grasp the waste receptacle, lift, and dump the waste receptacle into a waste-collection vehicle.

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St. Marys, Canada

Kenneth Alexander McIsaac