Abstract: Among other things, techniques are described for calibrating a sensor of a vehicle. One technique involves identifying a trigger for calibrating at least one sensor of a vehicle. In response to identifying the trigger, the technique then involves initiating a calibration path planning mode for the vehicle. In the calibration path planning mode, the technique involves: generating a plurality of calibration-aware paths that each include at least one calibration trajectory along at least one road; and selecting, from the plurality of paths, a first calibration-aware path for the vehicle, where the at least one sensor is calibrated while the vehicle travels along the selected path.

Abstract: An automatic calibration and validation pipeline is disclosed to estimate and evaluate the accuracy of extrinsic parameters of a camera-to-LiDAR coordinate transformation. In an embodiment, an automated and unsupervised calibration procedure is employed where the computed rotational and translational parameters (“extrinsic parameters”) of the camera-to-LiDAR coordinate transformation are automatically estimated and validated, and upper bounds on the accuracy of the extrinsic parameters are set. The calibration procedure combines three-dimensional (3D) plane, vector and point correspondences to determine the extrinsic parameters, and the resulting coordinate transformation is validated by analyzing the projection of a filtered point cloud including a validation target in the image space. A single camera image and LiDAR scan (a “single shot”) are used to calibrate and validate the extrinsic parameters.

Abstract: Provided are methods for using motion capture sensors to calibrate a component, which can include receiving motion capture sensor data associated with a motion capture sensor, the motion capture sensor data comprising at least a location of a motion capture marker and a location of a reference point on a vehicle, determining a position of a hardware component associated with the vehicle relative to the reference point on the vehicle based at least in part on the motion capture sensor data, determining the position of the hardware component does not satisfy a calibration threshold associated with the hardware component, determining a hardware component alert associated with the hardware component based at least in part on the determining that the position of the hardware component does not satisfy the calibration threshold associated with the hardware component, and routing the hardware component alert. Systems and computer program products are also provided.

Abstract: Among other things, techniques are described for a universal calibration target. The universal calibration target includes a core and an outer body. The core is core associated with a first salient property. The outer body is associated with a second salient property. The first salient property and the second salient property are configured to be observed by a sensor modality, and the first salient property and the second salient property correspond to at least one sensor of a vehicle.

Abstract: Among other things, techniques are described for calibrating a sensor of a vehicle. One technique involves identifying a trigger for calibrating at least one sensor of a vehicle. In response to identifying the trigger, the technique then involves initiating a calibration path planning mode for the vehicle. In the calibration path planning mode, the technique involves: generating a plurality of calibration-aware paths that each include at least one calibration trajectory along at least one road; and selecting, from the plurality of paths, a first calibration-aware path for the vehicle, where the at least one sensor is calibrated while the vehicle travels along the selected path.

Abstract: An automatic calibration and validation pipeline is disclosed to estimate and evaluate the accuracy of extrinsic parameters of a camera-to-LiDAR coordinate transformation. In an embodiment, an automated and unsupervised calibration procedure is employed where the computed rotational and translational parameters (“extrinsic parameters”) of the camera-to-LiDAR coordinate transformation are automatically estimated and validated, and upper bounds on the accuracy of the extrinsic parameters are set. The calibration procedure combines three-dimensional (3D) plane, vector and point correspondences to determine the extrinsic parameters, and the resulting coordinate transformation is validated by analyzing the projection of a filtered point cloud including a validation target in the image space. A single camera image and LiDAR scan (a “single shot”) are used to calibrate and validate the extrinsic parameters.