INS-SLAM Sensor Fusion for Drift-Corrected Pose Estimation
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Solution Overview
Problem
Inertial navigation systems (INS) are prone to integration drift, leading to accumulating errors in pose calculations over time, necessitating periodic corrections from external sources, especially in environments without reliable global navigation satellite systems (GNSS) coverage.
Innovation Solution
A navigation system that combines a three-axis accelerometer and gyroscope for real-time inertial measurements with a simultaneous localization and mapping (SLAM) unit using exteroceptive sensors to estimate visual odometer pose changes, and a sensor fusion engine to correct and refine INS estimates, providing absolute positions and orientations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If INS uses dead reckoning to calculate pose over time, then navigation autonomy is improved, but integration drift causes accumulating errors in position and orientation
Solution Approach 1:
The patent combines INS dead reckoning with SLAM visual odometry into a unified navigation system. The sensor fusion engine merges inertial measurements (acceleration and angular velocity from accelerometers and gyroscopes) with visual pose estimates from the SLAM system, creating a hybrid navigation approach that leverages the autonomy of INS while correcting its drift through visual feedback.
Solution Approach 2:
The SLAM system provides visual feedback to correct INS drift. The visual odometry estimates from sequential image processing serve as feedback signals that are fed into the sensor fusion engine, which continuously adjusts the INS pose estimates to compensate for integration drift, thereby maintaining long-term accuracy without external GNSS references.
2Adaptability or versatility
If INS operates without external corrections, then independence from external sources is improved, but errors accumulate proportionally to time elapsed
Solution Approach 1:
The navigation system uses the visual environment itself as the reference source, making the system self-sufficient without requiring external GNSS infrastructure. The SLAM system processes images of the surrounding environment to generate pose estimates, allowing the system to navigate independently in GPS-denied environments while still correcting drift through self-observation of the visual scene.
3Measurement precision
If SLAM visual odometry is used to correct INS drift, then pose estimation accuracy is improved, but system complexity increases due to sensor fusion requirements
Solution Approach 1:
The sensor fusion engine serves multiple functions: it integrates inertial and visual data, corrects drift, and provides a unified pose estimate. The exteroceptive sensors perform dual roles by capturing images for both SLAM mapping and visual odometry calculations. This multi-functionality reduces the need for separate dedicated systems, managing complexity while maintaining accuracy.
Data Source
AI summary
A navigation system for a dynamic platform includes an inertial navigation system (INS) unit for measuring, in real-time, linear accelerations and angular velocities of the dynamic platform, and determining, using dead reckoning, initial estimates of current poses of the dynamic platform based on a previous pose of the dynamic platform and the linear accelerations and angular velocities of the dynamic platform. The navigation system further includes an exteroceptive sensor for acquiring sequential images of an environment in which the dynamic platform is traveling, a simultaneous localization and mapping (SLAM) unit for estimating visual odometer (VO) pose changes of the dynamic platform using the sequential images, and a sensor fusion engine for determining estimates of current poses of the dynamic platform based at least in part on the initial estimates of current poses determined by the INS unit and the VO pose changes estimated by the local sub-map tracker.