Multi-Sensor SLAM Fusion for Robust High-Precision Mapping
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Solution Overview
Problem
Existing SLAM technologies are susceptible to environmental conditions, leading to low precision and significant errors, particularly in environments with limited features or changing lighting, and fail to maintain high accuracy over time.
Innovation Solution
A multi-sensor fusion-based SLAM method that integrates point cloud data, image data, IMU data, and GNSS data, utilizing calibration and synchronization to generate high-precision localization information, followed by closed-loop detection to create accurate local and global maps.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple sensors (laser radar, ultrasonic sensor, camera) are used for SLAM, then measurement precision and reliability are improved, but device complexity increases
Solution Approach 1:
The system divides the sensing function into multiple specialized sensors (laser radar for range, ultrasonic sensor for proximity, camera for visual features) that operate independently but contribute to the overall SLAM function. Each sensor handles specific measurement tasks, improving precision while allowing modular integration that manages complexity through functional segmentation.
Solution Approach 2:
The patent combines multiple types of sensors (laser radar, ultrasonic sensor, camera) into a unified SLAM system that processes data from all sources simultaneously. This merging of different sensing modalities improves measurement precision and reliability by providing redundant and complementary information, while the integrated processing framework manages the overall system complexity.
2Reliability
If multiple sensors are integrated for SLAM, then reliability is improved, but device complexity increases
Solution Approach 1:
Each sensor in the system is optimized for its specific measurement function (laser radar for accurate range measurement, ultrasonic sensor for proximity detection, camera for visual feature extraction). This local optimization of sensor characteristics improves overall system reliability by ensuring each component performs its designated function with high reliability, while the specialized design reduces the complexity burden of integrating diverse sensor types.
3Measurement precision
If complex sensor fusion algorithms are used, then measurement precision is improved, but processing time increases
Solution Approach 1:
The system performs preliminary processing of sensor data including calibration, feature extraction, and initial alignment before executing the full SLAM algorithm. By preparing and pre-processing data from multiple sensors in advance, the system reduces the computational burden during real-time operation, maintaining high measurement precision while minimizing processing time and avoiding delays.
Data Source
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AI summary
The present invention provides a multi-sensor fusion-based Simultaneous Localization And Mapping (SLAM) mapping method and system for a server., comprising: obtaining a plurality of sensor data regarding a surrounding environment of a moving platform, the plurality of sensor data including point cloud data, image data, inertial measurement unit (IMU) data, and global navigation satellite system (GNSS) data; performing hierarchical processing on the plurality of sensor data to generate a plurality of localization information, wherein one sensor data corresponds to one localization information; obtaining target localization information of the moving platform based on the plurality of localization information; generating a high-precision local map based on the target localization information; and performing a closed-loop detection operation to the high-precision local map to obtain a high-precision global map of the moving platform. The present invention mitigates the technical problem in the related art that easy susceptibility to a surrounding environment leads to low precision.