Abstract: In an example, a method for assembly of a modular cable connector includes, at an end effector of a robotic insertion system, holding a cable insert module for insertion into a module slot of a module retainer. Two or more images are captured of the module retainer. Image processing is performed to identify a segmented image region corresponding to the module retainer and a virtual plane parallel to a face of the module retainer. The end effector is moved to an alignment pose determined based, at least in part, on the segmented image region and the virtual plane. The end effector is moved from the alignment pose toward an insertion pose. Upon insertion of the cable insert module into the module slot of the module retainer, the cable insert module is released from the end effector.

Abstract: A road sign interpretation system includes a front-facing camera mounted on or in a vehicle collecting image data of multiple road signs. A first convolutional neural network (CNN) receives the image data from the front-facing camera and yields a set of sign predictions including one or more sign text instances. A second CNN defining a text extractor receives the image data from the front-facing camera and extracts text candidates including the multiple sign text instances. Sign and sign data localization is provided in the second CNN to compute a text order from the multiple sign text instances. A sign text synthesizer module receives individual sign text instances from the first CNN and individual ones of the sign text instances in digitized forms from an optical character recognizer (OCR). A semantic encoding and interpretation module receives the sign text instances and identifies semantics of the multiple road signs.

Abstract: A road sign interpretation system includes a front-facing camera mounted on or in a vehicle collecting image data of multiple road signs. A first convolutional neural network (CNN) receives the image data from the front-facing camera and yields a set of sign predictions including one or more sign text instances. A second CNN defining a text extractor receives the image data from the front-facing camera and extracts text candidates including the multiple sign text instances. Sign and sign data localization is provided in the second CNN to compute a text order from the multiple sign text instances. A sign text synthesizer module receives individual sign text instances from the first CNN and individual ones of the sign text instances in digitized forms from an optical character recognizer (OCR). A semantic encoding and interpretation module receives the sign text instances and identifies semantics of the multiple road signs.

Abstract: A method for updating a localization of an autonomous vehicle includes receiving perception input data from a sensor of the autonomous vehicle and receiving map data including road lane information in a vicinity of the autonomous vehicle. The method includes processing the perception input data to extract perceived road lane information including a perceived x position, a perceived y position, a perceived z position, a perceived lane type, a perceived lane color, and a perceived lane curvature and processing the map data to extract map road lane information including a map x position, a map y position, a map z position, a map lane type, a map lane color, and a map lane curvature. The method includes calculating a transformation matrix from the perceived road lane information and the map road lane information and updating the map data and a localization of the vehicle based on the transformation matrix.

Abstract: A method for updating a localization of an autonomous vehicle includes receiving perception input data from a sensor of the autonomous vehicle and receiving map data including road lane information in a vicinity of the autonomous vehicle. The method includes processing the perception input data to extract perceived road lane information including a perceived x position, a perceived y position, a perceived z position, a perceived lane type, a perceived lane color, and a perceived lane curvature and processing the map data to extract map road lane information including a map x position, a map y position, a map z position, a map lane type, a map lane color, and a map lane curvature. The method includes calculating a transformation matrix from the perceived road lane information and the map road lane information and updating the map data and a localization of the vehicle based on the transformation matrix.