Abstract: A computer includes a processor and a memory, and the memory stores instructions executable by the processor to receive at least one image frame from at least one camera, the at least one image frame showing a vehicle; determine at least one baseline pixel coordinate within one image frame for the vehicle; initialize a plurality of initial poses for the vehicle in a preset pattern; for each initial pose, determine a respective final pose by minimizing a reprojection error between the at least one baseline pixel coordinate and at least one respective estimated pixel coordinate, the at least one respective estimated pixel coordinate resulting from reprojecting the vehicle to pixel coordinates in the one image frame; and select a first final pose from the final poses, the first final pose having the lowest minimized reprojection error of the final poses.

Abstract: A computer includes a processor and a memory, and the memory stores instructions executable by the processor to receive an image frame from a camera, the image frame showing a vehicle, determine a two-dimensional bounding box around the vehicle in the image frame, determine a first three-dimensional point that is an intersection between (a) a ray extending from the camera through a center of the two-dimensional bounding box and (b) a first plane that is parallel to a ground plane, determine a second three-dimensional point on the vehicle, and determine an orientation of the vehicle based on the first three-dimensional point, the second three-dimensional point, and the ground plane. The ground plane represents a surface on which the vehicle sits.

Abstract: An approximate camera location on a route can be determined by inputting a first image acquired by a vehicle camera to a first convolutional neural network. First image feature points can be extracted from the first image using a feature extraction algorithm. Pose estimation parameters for a pose estimation algorithm can be selected based on the approximate camera location. A six degree-of-freedom (DoF) camera pose can be determined by inputting the first image feature points and second feature points included in a structure-from-motion (SfM) map based on the route to the pose estimation algorithm which is controlled by the pose estimation parameters. A six DoF vehicle pose can be determined based on the six DoF camera pose.

Abstract: A first plurality of center points of first two-dimensional bounding boxes corresponding to a vehicle occurring in a first plurality of images acquired by a first camera can be determined. A second plurality of center points of second two-dimensional bounding boxes corresponding to the vehicle occurring in a second plurality of images acquired by a second camera can also be determined. A plurality of non-linear equations based on the locations of the first and second pluralities of center points and first and second camera parameters corresponding to the first and second cameras can be determined. The plurality of non-linear equations can be solved simultaneously for the locations of the vehicle with respect to the first and second cameras and the six degree of freedom pose of the second camera with respect to the first camera.

Abstract: A computer includes a processor and a memory storing instructions executable by the processor to receive radar data including a radar pixel having a radial velocity from a radar; receive camera data including an image frame including camera pixels from a camera; map the radar pixel to the image frame; generate a region of the image frame surrounding the radar pixel; determine association scores for the respective camera pixels in the region; select a first camera pixel of the camera pixels from the region, the first camera pixel having a greatest association score of the association scores; and calculate a full velocity of the radar pixel using the radial velocity of the radar pixel and a first optical flow at the first camera pixel. The association scores indicate a likelihood that the respective camera pixels correspond to a same point in an environment as the radar pixel.

Abstract: A computer includes a processor and a memory, and the memory stores instructions executable by the processor to determine a quantity of relative motion between a host vehicle and a target vehicle; in response to the quantity of relative motion being below a threshold, determine a pose of the host vehicle subject to a constraint on motion of the host vehicle; and in response to the quantity of relative motion being above the threshold, determine the pose of the host vehicle without the constraint.

Abstract: An image can be input to a deep neural network to determine a point in the image based on a center of a Gaussian heatmap corresponding to an object included in the image. The deep neural network can determine an object descriptor corresponding to the object and include the object descriptor in an object vector attached to the point. The deep neural network can determine object parameters including a three-dimensional location of the object in global coordinates and predicted pixel offsets of the object. The object parameters can be included in the object vector, and the deep neural network can predict a future location of the object in global coordinates based on the point and the object vector.

Abstract: A computer includes a processor and a memory storing instructions executable by the processor to receive radar data from a radar, the radar data including radar pixels having respective measured depths; receive camera data from a camera, the camera data including an image frame including camera pixels; map the radar pixels to the image frame; generate respective regions of the image frame surrounding the respective radar pixels; for each region, determine confidence scores for the respective camera pixels in that region; output a depth map of projected depths for the respective camera pixels based on the confidence scores; and operate a vehicle including the radar and the camera based on the depth map. The confidence scores indicate confidence in applying the measured depth of the radar pixel for that region to the respective camera pixels.

Abstract: A computer that includes a processor and a memory, the memory including instructions executable by the processor to generate radar data by projecting radar returns of objects within a scene onto an image plane of camera data of the scene based on extrinsic and intrinsic parameters of a camera and extrinsic parameters of a radar sensor to generate the radar data. The image data can be received at an image channel of an image/radar convolutional neural network (CNN) and receive the radar data at a radar channel of the image/radar CNN, wherein features are transferred from the image channel to the radar channel at multiple stages Image object features and image confidence scores can be determined by the image channel, and radar object features and radar confidences by the radar channel. The image object features can be combined with the radar object features using a weighted sum.

Abstract: A plurality of virtual three-dimensional points distributed on a 3D reference plane for a camera array including a plurality of cameras are randomly selected. The plurality of cameras includes a host camera and one or more additional cameras. Respective two-dimensional projections of the plurality of virtual 3D points for the plurality of cameras are determined based on respective poses of the cameras. For the respective one or more additional cameras, respective homography matrices are determined based on the 2D projections for the respective camera and the 2D projections for the host camera. The respective homography matrices map the 2D projections for the respective camera to the 2D projections for the host camera. A stitched image is generated based on respective images captured by the plurality of cameras and the respective homography matrices.

Abstract: A depth image of an object can be input to a deep neural network to determine a first four degree-of-freedom pose of the object. The first four degree-of-freedom pose and a three-dimensional model of the object can be input to a silhouette rendering program to determine a first two-dimensional silhouette of the object. A second two-dimensional silhouette of the object can be determined based on thresholding the depth image. A loss function can be determined based on comparing the first two-dimensional silhouette of the object to the second two-dimensional silhouette of the object. Deep neural network parameters can be optimized based on the loss function and the deep neural network can be output.

Abstract: A computer is programmed to receive image data from a sensor; generate a feature pyramid from the image data, the feature pyramid including a plurality of features; apply a plurality of preliminary bounding boxes to the features to generate a plurality of preliminarily bounded features, each preliminarily bounded feature being a pairing of one of the preliminary bounding boxes and one of the features; execute a machine-learning program on the preliminarily bounded features to determine a plurality of classifications and a respective plurality of predicted bounding boxes; and actuate a component of a machine, e.g., a vehicle, based on the classifications and the predicted bounding boxes. The machine-learning program is a two-stage object detector having a first stage and a second stage. The first stage selects a subset of the preliminarily bounded features to pass to the second stage.

Abstract: A computer includes a processor and a memory, and the memory stores instructions executable by the processor to receive an image frame from a camera, the image frame showing a vehicle, determine a two-dimensional bounding box around the vehicle in the image frame, determine a first three-dimensional point that is an intersection between (a) a ray extending from the camera through a center of the two-dimensional bounding box and (b) a first plane that is parallel to a ground plane, determine a second three-dimensional point on the vehicle, and determine an orientation of the vehicle based on the first three-dimensional point, the second three-dimensional point, and the ground plane. The ground plane represents a surface on which the vehicle sits.

Abstract: A computer includes a processor and a memory, and the memory stores instructions executable by the processor to jointly train a geometric NeRF multilayer perceptron (MLP) and a color NeRF MLP to model a scene using an occupancy grid map, camera data of the scene from a camera, and lidar data of the scene from a lidar; supervise the geometric NeRF MLP with the lidar data during the joint training; and supervise the color NeRF MLP with the camera data during the joint training. The geometric NeRF MLP is a neural radiance field modeling a geometry of the scene, and the color NeRF MLP is a neural radiance field modeling colors of the scene.

Abstract: A robot can be moved in a structure that includes a plurality of downward-facing cameras, and, as the robot moves, upward images can be captured with an upward-facing camera mounted to the robot. Downward images can be captured with the respective downward-facing cameras. Upward-facing camera poses can be determined at respective times based on the upward images. Further, respective poses of the downward-facing cameras can be determined based on (a) describing motion of the robot from the downward images, and (b) the upward-facing camera poses determined from the upward images.

Abstract: A plurality of agent locations can be determined at a plurality of time steps by inputting a plurality of images to a perception algorithm that inputs the plurality of images and outputs agent labels and the agent locations. A plurality of first uncertainties corresponding to the agent locations can be determined at the plurality of time steps by inputting the plurality of agent locations to a filter algorithm that inputs the agent locations and outputs the plurality of first uncertainties corresponding to the plurality of agent locations. A plurality of predicted agent trajectories and potential trajectories corresponding to the predicted agent trajectories can be determined by inputting the plurality of agent locations at the plurality of time steps and the first uncertainties corresponding to the agent locations at the plurality of time steps to a variational autoencoder.

Abstract: A method may include: receiving a first image captured by a camera at a first time instance, wherein the first image includes at least a portion of an observed vehicle; determining a first ray angle based on a coordinate system of an ego-vehicle and a coordinate system of the observed vehicle corresponding to the first image; receiving a second image captured by the camera at a second time instance, wherein the second image includes at least a portion of the observed vehicle oriented at a different viewpoint; determining a second ray angle based on a coordinate system of the ego-vehicle and the coordinate system of the observed vehicle corresponding to the second image; determining a local angle difference based on the first ray angle and the second ray angle; and training a deep neural network using the local angle difference, the first image, and the second image.

Abstract: A computer includes a processor and a memory, and the memory stores instructions executable by the processor to receive at least one image frame from at least one camera, the at least one image frame showing a vehicle; determine at least one baseline pixel coordinate within one image frame for the vehicle; initialize a plurality of initial poses for the vehicle in a preset pattern; for each initial pose, determine a respective final pose by minimizing a reprojection error between the at least one baseline pixel coordinate and at least one respective estimated pixel coordinate, the at least one respective estimated pixel coordinate resulting from reprojecting the vehicle to pixel coordinates in the one image frame; and select a first final pose from the final poses, the first final pose having the lowest minimized reprojection error of the final poses.

Abstract: A computer, including a processor and a memory, the memory including instructions to be executed by the processor to capture, from a camera, one or more images, wherein the one or more images include at least a portion of a vehicle, receive a plurality of keypoints corresponding to markers on the vehicle and instantiate a virtual vehicle corresponding to the vehicle. The instructions include further instructions to determine rotational and translation parameters of the vehicle by matching a plurality of virtual keypoints to the plurality of keypoints and determine a multi-degree of freedom (MDF) pose of the vehicle based on the rotational and translation parameters.

Abstract: A two-dimensional image segment that includes an outline of an object can be determined in a top-down fisheye image. A six degree of freedom (DoF) pose for the object can be determined based on determining a three-dimensional bounding box determined by one or more of (1) an axis of the two-dimensional image segment in a ground plane included in the top-down fisheye image and a three-dimensional model of the object and (2) inputting the two-dimensional image segment to a deep neural network trained to determine a three-dimensional bounding box for the object.