Abstract: Example embodiments relate to identification of proxy calibration targets for a fleet of sensors. An example method includes collecting, using a sensor coupled to a vehicle, data about one or more objects within an environment of the vehicle. The sensor has been calibrated using a ground-truth calibration target. The method also includes identifying, based on the collected data, at least one candidate object, from among the one or more objects, to be used as a proxy calibration target for other sensors coupled to vehicles within a fleet of vehicles. Further, the method includes providing, by the vehicle, data about the candidate object for use by one or more vehicles within the fleet of vehicles.

Abstract: A method includes monitoring, at a computing device, outputs of a sensor validator. Each output is generated by the sensor validator based on corresponding sensor data from a sensor coupled to an autonomous vehicle, and each output indicates whether the corresponding sensor data is associated with an event. The method also includes mutating, at the computing device, particular sensor data to generate mutated sensor data that is associated with a particular event. The method further includes determining, at the computing device, a performance metric associated with the sensor validator based on a particular output generated by the sensor validator. The particular output is based on the mutated sensor data.

Abstract: Example embodiments relate to identification of proxy calibration targets for a fleet of sensors. An example method includes collecting, using a sensor coupled to a vehicle, data about one or more objects within an environment of the vehicle. The sensor has been calibrated using a ground-truth calibration target. The method also includes identifying, based on the collected data, at least one candidate object, from among the one or more objects, to be used as a proxy calibration target for other sensors coupled to vehicles within a fleet of vehicles. Further, the method includes providing, by the vehicle, data about the candidate object for use by one or more vehicles within the fleet of vehicles.

Abstract: Example embodiments relate to identification of proxy calibration targets for a fleet of sensors. An example method includes collecting, using a sensor coupled to a vehicle, data about one or more objects within an environment of the vehicle. The sensor has been calibrated using a ground-truth calibration target. The method also includes identifying, based on the collected data, at least one candidate object, from among the one or more objects, to be used as a proxy calibration target for other sensors coupled to vehicles within a fleet of vehicles. Further, the method includes providing, by the vehicle, data about the candidate object for use by one or more vehicles within the fleet of vehicles.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for determining an alignment between cross-modal sensor data. In one aspect, a method comprises: obtaining (i) an image that characterizes a visual appearance of an environment, and (ii) a point cloud comprising a collection of data points that characterizes a three-dimensional geometry of the environment; processing each of a plurality of regions of the image using a visual embedding neural network to generate a respective embedding of each of the image regions; processing each of a plurality of regions of the point cloud using a shape embedding neural network to generate a respective embedding of each of the point cloud regions; and identifying a plurality of region pairs using the embeddings of the image regions and the embeddings of the point cloud regions.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for determining an alignment between cross-modal sensor data. In one aspect, a method comprises: obtaining (i) an image that characterizes a visual appearance of an environment, and (ii) a point cloud comprising a collection of data points that characterizes a three-dimensional geometry of the environment; processing each of a plurality of regions of the image using a visual embedding neural network to generate a respective embedding of each of the image regions; processing each of a plurality of regions of the point cloud using a shape embedding neural network to generate a respective embedding of each of the point cloud regions; and identifying a plurality of region pairs using the embeddings of the image regions and the embeddings of the point cloud regions.

Abstract: Example embodiments relate to identification of proxy calibration targets for a fleet of sensors. An example method includes collecting, using a sensor coupled to a vehicle, data about one or more objects within an environment of the vehicle. The sensor has been calibrated using a ground-truth calibration target. The method also includes identifying, based on the collected data, at least one candidate object, from among the one or more objects, to be used as a proxy calibration target for other sensors coupled to vehicles within a fleet of vehicles. Further, the method includes providing, by the vehicle, data about the candidate object for use by one or more vehicles within the fleet of vehicles.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for determining an alignment between cross-modal sensor data. In one aspect, a method comprises: obtaining (i) an image that characterizes a visual appearance of an environment, and (ii) a point cloud comprising a collection of data points that characterizes a three-dimensional geometry of the environment; processing each of a plurality of regions of the image using a visual embedding neural network to generate a respective embedding of each of the image regions; processing each of a plurality of regions of the point cloud using a shape embedding neural network to generate a respective embedding of each of the point cloud regions; and identifying a plurality of region pairs using the embeddings of the image regions and the embeddings of the point cloud regions.

Abstract: An apparatus for transporting a load onto a raised surface includes: a frame; and rear, middle, and front wheel assemblies coupled to the frame. Each wheel assembly includes: at least one leg, at least one wheel rotatingly coupled to the leg for rollingly supporting the frame, and at least one actuator operatively coupled to the at least one leg. The front wheel assembly is configured to extend forwardly from, and retract rearwardly toward, the frame. The actuators are configured to independently raise and lower the at least one rear, middle, and front wheel. A load support member is located below the frame and moveable between a first position where the center of gravity of the load is located rearward of an axis defined by the at least one middle wheel, and a second position where the center of gravity of the load is located forward of the axis.

Abstract: An apparatus and method for reducing noise in a depth image are provided. The method includes capturing an image, generating a depth map of the image, the depth map including depth information corresponding to at least one object included in the image, performing over-segmentation of the depth map to generate segmentation information corresponding to the image, determining at least one segment to be noise according to the over-segmentation, and deleting the segment determined to be noise.

Abstract: An apparatus and method for reducing noise in a depth image are provided. The method includes capturing an image, generating a depth map of the image, the depth map including depth information corresponding to at least one object included in the image, performing over-segmentation of the depth map to generate segmentation information corresponding to the image, determining at least one segment to be noise according to the over-segmentation, and deleting the segment determined to be noise.

Abstract: An apparatus for transporting a load onto a raised surface includes: a frame; and rear, middle, and front wheel assemblies coupled to the frame. Each wheel assembly includes: at least one leg, at least one wheel rotatingly coupled to the leg for rollingly supporting the frame, and at least one actuator operatively coupled to the at least one leg. The front wheel assembly is configured to extend forwardly from, and retract rearwardly toward, the frame. The actuators are configured to independently raise and lower the at least one rear, middle, and front wheel. A load support member is located below the frame and moveable between a first position where the centre of gravity of the load is located rearward of an axis defined by the at least one middle wheel, and a second position where the centre of gravity of the load is located forward of the axis.

Abstract: The transportation apparatus includes a frame a seat portion which slides on the frame. A leg support pivots about the seat portion. At least one middle leg and at least one rear leg depends downwardly from the seat portion. At least one middle wheel is connected to the middle leg and at least one rear wheel is connected to the rear leg. The middle wheel and rear wheel define a middle and rear rotation axes, respectively. At least one front wheel is connected to the leg support. The middle leg is movable to adjust the vertical position of the middle wheel, and the rear leg is movable to adjust the vertical position of the rear wheel.

Abstract: A transportation apparatus includes a backrest and leg support pivotably connected to a seat portion. The seat portion is slidably connected to first and second spaced apart slide rails. Each slide rail includes a plurality of telescoping members. At least one middle leg and at least one rear leg depends downwardly from the seat portion. At least one middle wheel is connected to the middle leg and at least one rear wheel is connected to the rear leg. The middle wheel and rear wheel define a middle and rear rotation axes, respectively. At least one front wheel is operatively connected to the leg support. The middle leg is movable to adjust the vertical position of the middle wheel, and the rear leg is movable to adjust the vertical position of the rear wheel. The plurality of telescoping members slide the seat portion in a substantially horizontal direction in relation to the rear rotation axis or the middle rotation axis.

Abstract: A transportation apparatus for transporting an invalid onto a raised surface is disclosed. The transportation apparatus includes a backrest and leg support pivotably connected to a seat portion. The seat portion is slidably connected to first and second spaced apart slide rails. Each slide rail includes a plurality of telescoping members. At least one middle leg and at least one rear leg depends downwardly from the seat portion. At least one middle wheel is connected to the middle leg and at least one rear wheel is connected to the rear leg. The middle wheel and rear wheel define a middle and rear rotation axes, respectively. At least one front wheel is operatively connected to the leg support. The middle leg is movable to adjust the vertical position of the middle wheel, and the rear leg is movable to adjust the vertical position of the rear wheel.