US 12,327,401 B2
Sensor data annotation for training machine perception models
Jeffrey William Barker, Pittsburgh, PA (US); and Brendan Meeder, Pittsburgh, PA (US)
Assigned to AURORA OPERATIONS, INC., Pittsburgh, PA (US)
Filed by Aurora Operations, Inc., Mountain View, CA (US)
Filed on Aug. 23, 2022, as Appl. No. 17/893,838.
Prior Publication US 2024/0071060 A1, Feb. 29, 2024
Int. Cl. G06V 10/778 (2022.01); G06V 10/46 (2022.01); G06V 10/774 (2022.01)
CPC G06V 10/7788 (2022.01) [G06V 10/46 (2022.01); G06V 10/774 (2022.01)] 26 Claims
OG exemplary drawing
 
1. A computer-implemented method for data annotation for training machine perception models, comprising:
(a) receiving source sensor data descriptive of an object, the source sensor data having a source reference frame of at least three dimensions, wherein the source sensor data comprises annotated data associated with the object, the source sensor data comprising point cloud data collected by aggregating a plurality of ranging measurements over time;
(b) receiving target sensor data descriptive of the object, the target sensor data having a target reference frame of at least two dimensions, the target sensor data comprising two-dimensional image data;
(c) providing an input to a machine-learned boundary recognition model, wherein the input comprises the target sensor data and a projection of the source sensor data into the target reference frame; and
(d) determining, using the machine-learned boundary recognition model, a bounded portion of the target sensor data, wherein the bounded portion indicates a subset of the target sensor data descriptive of the object.