GROUND SURFACE ESTIMATION USING DEPTH INFORMATION FOR AUTONOMOUS SYSTEMS AND APPLICATIONS

In various examples, a surface may be estimated using depth data for autonomous systems and applications. One or more software components or modules may use the depth data (e.g., 3D LiDAR point cloud data) in addition to ego-motion data (e.g., data representative of location, heading, speed, and/or pose of the ego-machine) to generate a non-parametric model of the ground or driving surface. In some embodiments, an iterative process may be used to generate and iteratively refine estimated surface values by minimizing (or approximating minimization of) a cost function that penalizes deviation between measured values and estimated values and/or deviations among adjacent measured values. The systems and applications described herein may include robust real-time or near real-time ground surface estimation relying on generated data, and may further include a large-scale offline ground surface estimation approach that is non-causal and uses (e.g., all) available data at once.