LANDMARK DETECTION USING CURVE FITTING FOR AUTONOMOUS DRIVING APPLICATIONS

摘要

In various examples, one or more deep neural networks (DNNs) are executed to regress on control points of a curve, and the control points may be used to perform a curve fitting operation – e.g., Bezier curve fitting – to identify landmark locations and geometries in an environment. The outputs of the DNN(s) may thus indicate the two-dimensional (2D) image-space and/or three-dimensional (3D) world-space control point locations, and post-processing techniques – such as clustering and temporal smoothing – may be executed to determine landmark locations and poses with precision and in real-time. As a result, reconstructed curves corresponding to the landmarks – e.g., lane line, road boundary line, crosswalk, pole, text, etc. – may be used by a vehicle to perform one or more operations for navigating an environment.