Robot Pose Confidence Evaluation Using Lidar Map Matching

The patent implements a feedback mechanism by calculating a confidence level for the determined pose and using this feedback to iteratively optimize the pose. The confidence level serves as a quality metric that guides further refinement, allowing the system to assess and improve its own measurements until a satisfactory accuracy threshold is achieved.

The patent performs preliminary actions by first obtaining an initial pose determination through conventional methods, then using this preliminary result as a basis for subsequent optimization. The initial pose serves as a starting point that is refined through iterative confidence-based adjustments, combining simplicity with eventual precision.

The patent performs a preliminary pose determination using fast conventional methods, then uses this preliminary result as a basis for subsequent optimization. This allows the system to quickly obtain an initial estimate and then refine it only if necessary, minimizing time loss while improving precision.

The patent applies partial optimization by performing confidence-based pose refinement only when the initial pose determination does not meet the required confidence threshold. This selective approach avoids unnecessary computational overhead while ensuring sufficient relocation precision when needed.

The patent introduces a confidence level assessment that provides feedback on the reliability of the determined pose. This confidence metric enables the system to evaluate its own measurements and determine whether further optimization is necessary, thereby improving reliability through self-assessment.

The system performs self-assessment by calculating its own confidence level for the determined pose without requiring external validation. This self-service mechanism allows the robot to autonomously evaluate the quality of its pose determination and initiate refinement when needed, enhancing reliability independently.