Robot Trajectory Planning via Loop Closure and Parameter Tuning
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Traditional SLAM-based indoor localization and navigation for service robots require numerous high-definition cameras, leading to high costs and inefficiencies, especially when environments need to be re-mapped, as the existing methods are not optimized for adjusting parameters effectively to achieve accurate trajectories.
Innovation Solution
A trajectory planning method and system that utilizes a loop closure algorithm and cartographer localization algorithm to calculate and compare optimized and reference trajectories, adjusting parameters based on sensing data from LiDAR, IMU, and odometer to minimize pose errors and achieve alignment between the two trajectories, thereby optimizing the localization parameters for the self-propelled device.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional SLAM with multiple high-definition cameras is used to obtain high-resolution images and construct indoor localization and navigation charts, then measurement precision and reliability are improved, but device complexity and cost increase significantly
Solution Approach 1:
The patent extracts the essential function of trajectory acquisition from the complex multi-camera system and implements it through a simplified single-camera or sensor-based approach. By taking out the redundant camera components while retaining the core localization function, the system achieves acceptable trajectory accuracy without the hardware complexity of traditional SLAM systems
Solution Approach 2:
The patent changes the parameters of the cartographer localization algorithm through iterative optimization. By adjusting algorithm parameters and using loop closure detection to correct drift, the system compensates for the reduced sensor capabilities, achieving accurate trajectory reconstruction without requiring multiple high-definition cameras
2Measurement precision
If multiple high-definition cameras are erected to cover larger fields and obtain high-resolution images, then measurement precision is improved, but device complexity and cost increase
Solution Approach 1:
The patent uses a single camera or sensor to capture data, then creates multiple virtual views and reconstructs the trajectory through computational methods. Instead of physically deploying multiple cameras, the system digitally synthesizes the information needed for accurate localization, reducing hardware complexity while maintaining measurement precision
Solution Approach 2:
The patent replaces the mechanical system of multiple physical cameras with a computational system. By using algorithms to process data from a single sensor and reconstruct trajectories through loop closure detection and parameter optimization, the system substitutes physical complexity with computational intelligence
3Adaptability or versatility
If all cameras need to be erected again when a field needs to be replaced, then adaptability is reduced, but device complexity increases due to reconfiguration requirements
Solution Approach 1:
The patent creates a universal localization system that can operate in different environments using the same single-camera or sensor platform. The cartographer algorithm and loop closure detection mechanism are environment-agnostic, allowing the system to be deployed in new fields without reconfiguring hardware, thus improving adaptability while reducing the time and effort required for re-mapping
Data Source
AI summary
The following steps are executed by a self-propelled device and a calculating device: the self-propelled device moves on a path to form a moving trajectory; the calculating device obtains a sensing data file generated by a sensing module of the self-propelled device on the path; after the sensing data file is imported into a simultaneous localization and mapping algorithm, a map corresponding to the path can be constructed; if the sensing data file is imported into a loop closure algorithm, a pose at each unit time can be obtained, and an optimized trajectory can be formed; when the sensing data file is imported into a cartographer localization algorithm, a reference trajectory can be constructed, and whether the reference trajectory approaches an optimized trajectory is determined; and parameters capable of being imported into the self-propelled device are obtained according to the reference trajectory which approaches the optimized trajectory.