INS-SLAM Sensor Fusion for Drift-Corrected Pose Estimation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Inertial navigation systems (INS) suffer from integration drift, where small errors in acceleration and angular velocity measurements accumulate over time, leading to progressively larger errors in pose calculation, necessitating periodic corrections from external sources.
Innovation Solution
A navigation system that combines an INS unit with a simultaneous localization and mapping (SLAM) unit, using exteroceptive sensors like lidars or cameras to acquire images and estimate visual odometer pose changes, which are then fused with INS data by a sensor fusion engine to correct and refine position and orientation estimates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If an INS unit is used for navigation, then autonomous navigation capability is improved, but integration drift errors accumulate over time
Solution Approach 1:
The patent combines INS with SLAM system to create a hybrid navigation system. The INS provides continuous pose estimates while the SLAM system provides periodic corrections, merging the advantages of both systems to maintain autonomous navigation capability while reducing integration drift errors.
Solution Approach 2:
The SLAM system provides feedback corrections to the INS system. By comparing the INS-derived pose with the SLAM-derived pose and applying corrections, the system continuously reduces accumulated errors while maintaining autonomous operation.
2Measurement precision
If SLAM is used to correct INS drift, then pose estimation accuracy is improved, but system complexity increases
Solution Approach 1:
The SLAM system serves multiple functions: it provides pose estimation for navigation correction, builds environmental maps, and enables autonomous operation without external references. This multi-functionality justifies the added complexity by delivering comprehensive navigation capabilities.
3Measurement precision
If external references like GNSS are used for correction, then integration drift is reduced, but reliability decreases in poor coverage environments
Solution Approach 1:
The SLAM system enables the navigation system to correct its own drift using onboard sensors and environmental features rather than external references. The system builds and uses its own map for pose correction, making it self-sufficient and reliable in environments where GNSS coverage is poor or unavailable.
Data Source
Figure 1
Figure 2A~2B
Figure 3A~3B
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.