Abstract: A method for visual inertial odometry (VIO)-aided global positioning is described. The method includes updating an extended Kalman filter (EKF) state including a current pose and a sliding window of multiple prior poses. The sliding window includes poses at a number of most recent global positioning system (GPS) time epochs. Updating the EKF includes updating an EKF covariance matrix for the prior poses and the current pose in the EKF state. The method also includes determining, at a GPS epoch, a relative displacement between each of the updated prior poses and the current pose. The method further includes determining an error covariance of each of the relative displacements based on cross-covariances between each of the updated prior poses and the current pose in the EKF covariance matrix. The method additionally includes using the relative displacements and the error covariances to fuse pseudorange measurements taken over multiple epochs.

Abstract: A method for visual inertial odometry (VIO)-aided global positioning is described. The method includes updating an extended Kalman filter (EKF) state including a current pose and a sliding window of multiple prior poses. The sliding window includes poses at a number of most recent global positioning system (GPS) time epochs. Updating the EKF includes updating an EKF covariance matrix for the prior poses and the current pose in the EKF state. The method also includes determining, at a GPS epoch, a relative displacement between each of the updated prior poses and the current pose. The method further includes determining an error covariance of each of the relative displacements based on cross-covariances between each of the updated prior poses and the current pose in the EKF covariance matrix. The method additionally includes using the relative displacements and the error covariances to fuse pseudorange measurements taken over multiple epochs.

Abstract: A method performed by an electronic device is described. The method includes determining a predicted velocity relative to Earth corresponding to a first epoch using a camera and an inertial measurement unit (IMU). The method also includes determining, using a Global Positioning System (GPS) receiver, a GPS velocity relative to Earth. The method further includes determining a difference vector between the predicted velocity and the GPS velocity. The method additionally includes refining a bias estimate and a scale factor estimate of IMU measurements proportional to the difference vector. The method also includes refining a misalignment estimate between the camera and the IMU based on the difference vector. The method further includes providing pose information based on the refined bias estimate, the refined scale factor, and the refined misalignment estimate.

Abstract: An electronic device is described. The electronic device includes a memory and a processor in communication with the memory. The memory is configured to store precalibration data for a camera mounted on a vehicle, the precalibration data including a camera height determined relative to a road plane the vehicle is configured to contact during operation. The processor is configured to receive a plurality of images. The processor is also configured to classify one or more features in the plurality of images as road features based on the precalibration data.