Derivative works

DEFENSIVE DISCLOSURE AND PRIOR ART PUBLICATION

Title: Systems and Methods for Dynamic Calibration and Cross-Domain Application of Geometric Perception Sensors
Publication Date: April 26, 2026
Reference Patent: U.S. Patent 12,136,276
Keywords: Camera Calibration, Extrinsic Parameters, Deep Learning, Computer Vision, ADAS, Neuromorphic Sensing, Robotics, Autonomous Systems, Sensor Fusion, Failsafe Systems

Abstract: This document discloses a plurality of methods, systems, and applications derived from the core principles of using a deep learning model to determine the extrinsic parameters (e.g., height, pitch, roll, normal vectors) of a vision sensor relative to an environmental plane by identifying geometric features such as horizon lines or parallel guide lines. The disclosures herein are intended to enter the public domain as prior art.

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1. Material & Component Substitution Derivatives

1.1. Neuromorphic Event-Based Camera Initialization

• Enabling Description: The standard CMOS/RGB image sensor is replaced with an event-based neuromorphic vision sensor (e.g., a Dynamic Vision Sensor - DVS). This sensor does not capture frames but rather a stream of asynchronous "events" corresponding to changes in pixel-level brightness. The Convolutional Neural Network (CNN) is replaced by a Spiking Neural Network (SNN) architected to process this event stream. The SNN identifies the spatiotemporal signatures of lane markings and the horizon as the vehicle moves, detecting them as lines of high, correlated event activity. The SNN regresses the camera's extrinsic parameters with extremely low latency (<10ms) and power consumption, making it suitable for high-speed applications or battery-powered devices where constant recalibration is needed. The input to the network is not an image tensor but a sparse stream of (x, y, t, p) event tuples.

graph TD
    A[DVS Sensor] -- Event Stream (x,y,t,p) --> B[Spiking Neural Network];
    B -- Spatiotemporal Feature Extraction --> C{Horizon & Lane Spikes};
    C -- Geometric Inference Engine --> D[Camera Parameters (Height, Normal)];
    D -- Publish --> E[Vehicle Control System];

1.2. Thermal (LWIR) Camera for Adverse Conditions

• Enabling Description: An uncooled microbolometer sensor operating in the Long-Wave Infrared (LWIR) spectrum (8-14 µm) is used instead of a visible light camera. This allows the system to operate in complete darkness, fog, or heavy smoke where visible lane markings are obscured. The training dataset for the CNN comprises thermal imagery where lane markings are visible due to differential heat retention between the paint and the asphalt. The horizon is detected as the thermal boundary between the ground plane and the sky or distant terrain. The model is trained to be robust to thermal artifacts such as engine heat plumes from other vehicles.

sequenceDiagram
    participant LWIR_Cam as LWIR Camera
    participant CNN_Model as Thermal CNN
    participant GeoCalc as Geometry Calculator
    loop In Adverse Weather
        LWIR_Cam->>CNN_Model: Transmit Thermal Frame
        CNN_Model->>CNN_Model: Identify Horizon & Lane Thermal Signatures
        CNN_Model->>GeoCalc: Output Feature Coordinates
        GeoCalc->>GeoCalc: Compute Extrinsic Parameters
    end

1.3. FPGA-Based Reconfigurable Processing Pipeline

• Enabling Description: The processing is performed not on a general-purpose GPU/CPU but on a Field-Programmable Gate Array (FPGA). The CNN architecture is synthesized directly into hardware logic gates, creating a highly parallelized and power-efficient pipeline. This allows for the dynamic reconfiguration of the neural network architecture in the field. For example, the system can switch from a horizon-finding model (for open highways) to a dense lane-grid model (for parking lots) by loading a different bitstream into the FPGA, without requiring a system reboot. The geometric calculations are also implemented as hardware math blocks on the FPGA for deterministic, real-time performance.

classDiagram
    class FPGA {
        +LoadBitstream(bitstream)
    }
    class CNN_Hardware_Implementation {
        <<Bitstream>>
        +ProcessFrame(frame) : FeatureVector
    }
    class Geometry_Coprocessor {
        <<Hardware Block>>
        +CalculateParams(featureVector) : Extrinsics
    }
    FPGA *-- "1" CNN_Hardware_Implementation : contains
    FPGA *-- "1" Geometry_Coprocessor : contains

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2. Operational Parameter Expansion Derivatives

2.1. Microscopic Calibration for Automated Microscopy

• Enabling Description: The system is scaled down for calibrating the camera of a digital microscope relative to a substrate (e.g., a silicon wafer or biological slide). The "lanes" are micro-fabricated conductive traces or patterned cell cultures. The "horizon" is the edge of the substrate or a fiducial marker. A CNN analyzes the microscope's video feed to calculate the precise height (z-distance) and tilt of the objective lens relative to the sample plane. This enables automated focusing and repeatable, high-precision positioning for tasks like automated wafer inspection or high-throughput screening.

flowchart LR
    A[Microscope Camera] --> B(Image Frame);
    B --> C[CNN];
    C -- Detects Micro-Traces & Substrate Edge --> D{Feature Coordinates};
    D --> E[Parameter Calculation];
    E -- Z-Height & Planar Tilt --> F[Microscope Stage & Focus Control];

2.2. Gantry Crane Calibration in Industrial Environments

• Enabling Description: The technology is applied to a large-scale gantry crane in a port or manufacturing facility. A camera is mounted high on the crane's trolley, which can be 30-50 meters high. The "lanes" are painted safety walkways or container alignment guides on the ground. The system continuously calculates the camera's height and viewing angle, compensating for sway and vibration of the crane structure. This provides accurate positioning data to the crane's automation system for precise container handling, greatly improving safety and efficiency over systems that rely solely on encoders.

stateDiagram-v2
    [*] --> Idle
    Idle --> Calibrating: Crane Moves
    Calibrating: CNN analyzes ground markings
    Calibrating --> Calibrated: Horizon & 2+ lines found
    Calibrated: Outputting Height(z) & Normal(nx,ny,nz)
    Calibrated --> Calibrating: High sway detected
    Calibrated --> Idle: Crane Stops

2.3. Subsea ROV Navigation and Attitude Estimation

• Enabling Description: A camera on a remotely operated vehicle (ROV) on the seafloor uses this method for altitude and attitude estimation. The "lanes" are subsea pipelines, cable runs, or track marks from the ROV itself. The "horizon" is the transition between the visible seafloor and the dark, featureless water column at the edge of the ROV's lights. The CNN processes the sonar-like video feed to determine the ROV's height off the seabed and its pitch/roll, providing a crucial secondary navigation input that is immune to magnetic interference affecting compasses or drift in inertial sensors.

graph TD
    A[ROV Sonar/Camera] --> B{Video Feed};
    B --> C[CNN Model];
    C -- Identifies Pipeline & Seafloor Edge --> D[Parameter Computation];
    D -- Height, Pitch, Roll --> E[ROV Flight Control System];
    E -- Adjust Thrusters --> F[ROV];
    F --> A;

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3. Cross-Domain Application Derivatives

3.1. Aerospace: Planetary Rover Autonomous Navigation

• Enabling Description: A camera on a planetary rover (e.g., on Mars) uses this system to maintain calibration. The rover's own wheel tracks in the regolith serve as the "lane lines." The planetary horizon, which is sharp and clear in the thin atmosphere, is the primary feature. The CNN calculates the mast-mounted camera's height and orientation relative to the ground plane, compensating for thermal contraction/expansion of the mast and suspension articulation as the rover traverses uneven terrain. This ensures the accuracy of stereo vision-based obstacle avoidance and path planning.

sequenceDiagram
    participant RoverCam as Rover Camera
    participant NavCPU as Navigation Computer (CNN)
    participant MotorCtrl as Motor Controller
    RoverCam->>NavCPU: Capture Image of Terrain
    NavCPU->>NavCPU: Detect Rover Tracks & Martian Horizon
    NavCPU->>NavCPU: Calculate MastCam Height & Tilt
    NavCPU->>MotorCtrl: Send Updated Path Plan

3.2. AgTech: Precision Sprayer Boom Height Control

• Enabling Description: A camera is mounted on the boom of an agricultural sprayer. The rows of crops (e.g., corn, soybeans) are treated as "lane lines." A CNN processes the video feed to determine the precise height and angle of the sprayer boom relative to the crop canopy. This data is fed into the boom's hydraulic control system in real-time to maintain a perfect spraying height, which maximizes pesticide/fertilizer efficacy and minimizes drift, even as the tractor moves over uneven ground.

flowchart TD
    A[Boom-Mounted Camera] --> B[Image of Crop Rows];
    B --> C[CNN for Row & Canopy Detection];
    C --> D[Compute Height & Angle];
    D --> E[Hydraulic Control System];
    E --> F[Adjust Boom Actuators];

3.3. Retail: Automated Restocking Robot Navigation

• Enabling Description: An autonomous robot in a warehouse or retail store navigates aisles by treating the floor-level shelving or pallet boundaries as "lane lines." The system uses a CNN to calculate the height and pitch of its navigation camera relative to the floor. This allows the robot to accurately measure its distance to shelves and detect floor-based obstacles. The system can dynamically recalibrate if the robot's payload changes, causing its suspension to compress.

graph TD
    A[Robot Camera] -- Video Stream --> B((CNN Processor));
    B -- Detects Shelf Lines --> C{Geometric Analysis};
    C -- Calculates Height & Pose --> D[Path Planning Module];
    D -- Drive Commands --> E[Motor System];

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4. Integration with Emerging Tech Derivatives

4.1. AI-Driven Reinforcement Learning for Self-Correction

• Enabling Description: The camera initialization module is an agent in a Reinforcement Learning (RL) framework. The "state" includes the video feed and current camera parameters. The "action" is to either maintain the current parameters or trigger a recalibration. The "reward" is provided by a downstream driving performance monitor. A high negative reward (e.g., for lane departure or jerky steering) prompts the RL agent to recalibrate, assuming the parameters have drifted. Over time, the agent learns to predict parameter drift based on subtle visual cues or environmental conditions (e.g., road vibration patterns) and proactively recalibrates before performance degrades.

flowchart LR
    A[Video & Current Params] --> B(RL Agent);
    B -- Action: Recalibrate or Keep --> C(Lane Keeping System);
    C -- Performance Score --> D{Reward Function};
    D -- Reward Signal --> B;
    B -- New Optimal Params --> C;

4.2. IoT Sensor Fusion with IMU and GNSS

• Enabling Description: The CNN architecture is modified to accept multiple input modalities. In addition to the image tensor, it accepts time-synchronized data from an Inertial Measurement Unit (IMU) and a GNSS receiver. The network learns the complex correlations between visual horizon shifts and IMU-reported pitch/roll, as well as the relationship between lane perspective and GNSS-derived velocity vectors. The system can then detect inconsistencies; for example, if the visual horizon is stable but the IMU reports a large pitch change, it correctly deduces the vehicle is on a hill and adjusts the road plane normal accordingly, something a purely visual system would struggle with.

graph TD
    subgraph Sensor Inputs
        A[Camera]
        B[IMU]
        C[GNSS]
    end
    subgraph Fusion CNN
        D[Multi-Modal Feature Extractor]
    end
    A -- Image Tensor --> D;
    B -- Pitch/Roll/Yaw Rates --> D;
    C -- Velocity Vector --> D;
    D --> E[Parameter Regression Head];
    E --> F[Calibrated Extrinsics];

4.3. Blockchain for Verifiable Calibration Audits

• Enabling Description: For commercial vehicle fleets, each successful camera initialization or recalibration event is cryptographically signed and recorded on a private blockchain. The transaction record includes the vehicle ID, camera serial number, timestamp, the newly calculated parameters (height, normal vector), and a hash of the video segment used for calibration. This creates an immutable, tamper-proof log of the vehicle's ADAS sensor status. Fleet managers, insurance companies, and regulatory bodies can be granted access to this ledger to audit and verify that safety-critical systems are maintained and calibrated according to standards.

erDiagram
    VEHICLE ||--o{ CALIBRATION_EVENT : "has"
    VEHICLE {
        string VehicleID
        string PublicKey
    }
    CALIBRATION_EVENT {
        string EventID
        datetime Timestamp
        string CameraParams
        string VideoHash
        string Signature
    }

    CALIBRATION_EVENT ||--o{ BLOCKCHAIN_TRANSACTION : "is recorded in"
    BLOCKCHAIN_TRANSACTION {
        string TxHash
        int BlockNumber
    }

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5. "Inverse" or Failure Mode Derivatives

5.1. Graceful Degradation to "Bumper-Relative" Mode

• Enabling Description: In conditions where the CNN cannot confidently detect a horizon or lane markings (e.g., tunnels, whiteouts, urban canyons), the system enters a limited-functionality mode. It disables lane-keeping and uses a simpler object detection model to find the bottom-center point of the vehicle directly ahead. Assuming a flat road, it calculates a "time to bumper" distance based on the rate of change of this point's vertical position in the image. The system issues forward collision warnings but makes no assumptions about lane position, thus failing safely by reducing its operational scope.

stateDiagram-v2
    state "Full Functionality" as Full {
      [*] --> Normal
      Normal: Horizon & Lanes Detected
      Normal: Full ADAS enabled
    }
    state "Limited Mode" as Limited {
      [*] --> BumperRelative
      BumperRelative: No features detected
      BumperRelative: Lane Keep OFF
      BumperRelative: FCW only
    }
    Full --> Limited: Low Confidence Score
    Limited --> Full: Features Detected

5.2. Failsafe Cross-Validation with Physical Sensor

• Enabling Description: A secondary, low-cost physical sensor (e.g., an ultrasonic or single-point LiDAR sensor) is mounted with a fixed downward orientation to directly measure the distance to the road surface. The main system operates as described in the patent. A separate "Validator" module continuously compares the camera height calculated by the CNN (H_cnn) with the height measured by the physical sensor (H_phys). If |H_cnn - H_phys| > Threshold (e.g., > 10 cm) for a sustained period, the system flags the vision-based calibration as untrustworthy, disables dependent ADAS features, and logs a maintenance alert for the operator.

flowchart TD
    A[Camera] --> B(CNN);
    B -- Calculated Height (H_cnn) --> D{Validator};
    C[Ultrasonic Sensor] -- Measured Height (H_phys) --> D;
    D -- |H_cnn - H_phys| > Threshold? --> E{Decision};
    E -- No --> F[Enable ADAS];
    E -- Yes --> G[Disable ADAS & Alert];

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6. Combination Prior Art with Open-Source Standards

6.1. Combination with ROS (Robot Operating System)

• Enabling Description: The camera initialization system is packaged as a standard ROS 2 node named camera_calibrator. This node subscribes to a sensor_msgs/Image topic for video frames and a sensor_msgs/Imu topic. Upon initialization, it computes the extrinsic parameters and publishes them as a tf2_msgs/TFMessage transform between the base_link and camera_link frames. It also publishes the full camera intrinsic and extrinsic details on a sensor_msgs/CameraInfo topic. This allows any other ROS-compliant node, such as a path planner or object detector, to immediately use the calibrated camera data without custom integration.

6.2. Combination with OpenCV and ONNX Runtime

• Enabling Description: The trained CNN model is exported to the open standard ONNX (Open Neural Network Exchange) format. An on-vehicle C++ application uses the OpenCV library for video capture and pre-processing (e.g., cv::VideoCapture, cv::resize). The core inference is then performed using the ONNX Runtime, which is optimized for cross-platform execution on various hardware accelerators. The output tensors from the ONNX Runtime, representing the horizon/lane coordinates, are then fed into OpenCV's geometry functions (e.g., cv::solvePnP or custom homography calculations) to derive the final extrinsic parameters. This decouples the model training framework (e.g., PyTorch) from the deployment environment.

6.3. Combination with AUTOSAR (Automotive Open System Architecture)

• Enabling Description: The initialization logic is encapsulated within an AUTOSAR Adaptive Platform Application. It defines its service interfaces using the ARA::COM API. It provides a "CameraCalibrationService" that other applications (e.g., a LaneKeepingApplication) can discover and consume. The service exposes methods like get_camera_height() and get_road_normal_vector(). The application runs in its own sandboxed execution context, managed by the AUTOSAR Execution Management functional cluster, ensuring it meets automotive safety and real-time constraints (e.g., freedom from interference). Video data is received via a SOME/IP binding from a lower-level camera driver.