Inertial Tracking Trajectory Correction via Reinforcement Learning

The patent applies feedback by using reinforcement learning agents that continuously receive feedback about trajectory deviations from environmental constraints (walls, doors, furniture) and adjust predictions accordingly. The agent monitors the difference between predicted and actual object positions, using this feedback to correct cumulative errors in real-time trajectory tracking.

The patent replaces traditional mechanical sensor-based tracking systems with a computational approach using reinforcement learning agents. Instead of relying solely on physical sensors that accumulate errors, the system uses an intelligent agent that processes sensor data, environmental constraints, and object models to predict and correct trajectories, substituting mechanical measurement with computational intelligence.

The patent applies self-service by enabling the reinforcement learning agent to autonomously learn environmental constraints and object behaviors without requiring extensive pre-programmed ground truth data. The agent observes and adapts to the environment dynamically, using only sparse sensor measurements and general object models to achieve accurate localization, making the system self-calibrating and reducing infrastructure requirements.

The patent changes the fundamental parameters of the system by transitioning from a data-heavy approach requiring extensive ground truth points to a model-based approach using reinforcement learning. The system achieves high precision localization not through quantity of training data but through intelligent algorithms that efficiently extract meaningful patterns from sparse observations, fundamentally altering how localization is achieved.