SLAM Timestamp Correction for IMU-Camera Alignment
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Solution Overview
Problem
The inconsistency in temporal references between IMU and camera timestamps in SLAM systems leads to lower positioning accuracy due to hardware differences and algorithmic discrepancies, necessitating a method to align these timestamps for improved accuracy.
Innovation Solution
A SLAM positioning method that involves acquiring time compensation values, constructing a SLAM model based on these values, and iteratively optimizing the time deviation between IMU and camera timestamps to align their temporal references, using nonlinear optimization or Kalman-related filtering to estimate system states and time compensation values.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If independent time systems are used for IMU and camera modules, then hardware independence and modularity are maintained, but temporal reference inconsistency occurs leading to lower positioning accuracy
Solution Approach 1:
The patent introduces a time compensation value parameter that adjusts the camera timestamp to align with the IMU time system. By modifying the time parameter of camera data through compensation, the temporal reference inconsistency between independent IMU and camera modules is resolved, enabling accurate fusion of inertial and visual measurements for improved positioning accuracy
Solution Approach 2:
The patent uses a time compensation value as an intermediary element that mediates between the IMU time system and camera time system. This compensation value acts as a bridge to synchronize the temporal references of two independent modules without requiring changes to their underlying hardware time systems, thus maintaining modularity while achieving temporal alignment
2Reliability
If camera system time is slower than IMU system time, then hardware independence is maintained, but timestamp alignment error increases reducing SLAM positioning accuracy
Solution Approach 1:
The patent performs preliminary time compensation on camera timestamps before fusing them with IMU data in the SLAM framework. By pre-adjusting the camera time stamps using the time compensation value, the temporal alignment error is corrected in advance, ensuring that both data streams are synchronized when processed together for accurate positioning
Data Source
AI summary
A SLAM positioning method and apparatus based on timestamp correction, a device, and a storage medium are provided. The method includes: acquiring a time compensation value, an image observation result, and a system state of each sub-window in a current sliding window; constructing a SLAM model based on time compensation values, the image observation results and the system states of the sub-windows in the current sliding window, where the time compensation value is a time deviation between a time system of a camera and a time system of an IMU; solving the SLAM model to obtain a parameter to be estimated, including the system state and the time compensation value to be estimated of each sub-window in the current sliding window.