Camera-Based Depth Verification With Height Reference Points

The patent replaces the mechanical/optical LIDAR system with a computational approach using camera images and deep neural networks to estimate depth. Instead of using physical distance-measuring hardware, the system uses software algorithms that process visual data to infer depth information, thereby reducing hardware complexity while maintaining measurement capability.

The patent creates a virtual copy of the LIDAR measurement function through computational depth estimation. By training neural networks on labeled depth data and using them to predict depth from images, the system replicates the distance measurement capability of LIDAR without requiring the actual LIDAR hardware, thus reducing system complexity.

The patent implements a feedback mechanism where the depth estimation system's predictions are continuously evaluated against ground truth labels during training. This feedback loop allows the model to learn from its errors and improve its generalization capability, reducing overfitting and enhancing reliability in real-world deployment conditions that differ from training data.

The patent employs techniques to change model parameters dynamically, including data augmentation during training and potential fine-tuning in deployment. By adjusting parameters such as learning rates, regularization strengths, and model architecture configurations, the system optimizes its balance between fitting training data and generalizing to new conditions, thereby improving reliability.

The patent replaces expensive hardware systems with computational methods for verifying pixel correspondence. Instead of using additional sensors or complex calibration equipment, the system uses software-based verification that processes image data through neural networks and compares predictions against known ground truth annotations, achieving high precision without the hardware complexity.

The patent uses computationally inexpensive operations and software-based verification methods instead of expensive hardware. The verification process relies on processing existing image data through neural networks and comparing results against pre-labeled ground truth data, which can be done with standard computational resources rather than specialized expensive equipment.