Laser Scanner Ego-Motion Estimation via IMU Preliminary Action
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Solution Overview
Problem
Existing mapping systems for autonomous devices face challenges in generating robust, real-time maps while in motion, particularly due to motion distortions caused by slow laser scanning rates and the need for accurate GPS/INS solutions, which can be impractical in GPS-denied or cost-sensitive environments.
Innovation Solution
A modularized mapping system incorporating an inertial measurement unit (IMU), camera, and laser scanner, with a computing system that processes data from these sensors to perform real-time ego-motion estimation and map generation, using a three-layer voxel representation and multi-thread processing to handle sensor degradation and aggressive motion.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If laser scanning rate is slow, then device complexity is reduced, but motion distortion increases
Solution Approach 1:
The system performs preliminary motion estimation using IMU data before laser points are acquired, then applies this motion information to correct the laser point positions. This preliminary action compensates for motion effects that occur during the laser scanning process, allowing accurate mapping even at slower scanning rates.
Solution Approach 2:
The patent introduces an intermediary motion estimation module that processes IMU data and generates motion compensation information. This intermediary component bridges the gap between the slow laser scanner and the fast-moving platform, translating inertial measurements into corrective transformations for the laser point cloud.
2Measurement precision
If GPS/INS is used for accurate position determination, then position accuracy is improved, but device complexity and cost increase
Solution Approach 1:
The patent extracts and utilizes only the necessary inertial measurement components (accelerometers and gyroscopes) from a full GPS/INS system, separating these from the more complex GPS satellite reception and processing subsystems. This extraction maintains position accuracy while reducing overall system complexity and cost.
Solution Approach 2:
The system replaces expensive, complex GPS/INS infrastructure with more affordable inertial sensors that provide sufficient accuracy for the application. While IMU data requires processing, the lower hardware cost and simplified architecture make this a economically viable alternative to full GPS/INS systems.
3Reliability
If multiple sensors are integrated for robust mapping, then reliability is improved, but device complexity increases
Solution Approach 1:
The patent merges data from the laser scanner, IMU, and optional GPS/camera sensors into a unified processing pipeline. By combining these sensors and their data streams into a single integrated system that produces a combined point cloud output, the architecture achieves robustness without proportionally increasing complexity.
Solution Approach 2:
The processing system is designed to be universal and multi-functional, handling data from various sensor types (laser, IMU, GPS, cameras) through a common processing framework. This universal approach allows the system to adapt to different sensor configurations and environmental conditions without requiring separate processing paths for each sensor type.
4Productivity
If real-time processing is implemented, then productivity is improved, but measurement precision may deteriorate
Solution Approach 1:
The system performs preliminary processing of IMU data to estimate motion between laser scans before the actual point cloud registration occurs. This preliminary motion estimation allows the real-time system to compensate for motion effects without waiting for complete and computationally intensive post-processing, thereby maintaining both speed and accuracy.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The system achieves high-accuracy, real-time map generation and navigation without external location technologies, capable of handling sensor failures and operating in dynamic environments, with low drift and robustness over long distances.
Implementation Method 1
a laser scanning unit
Implementation Method 2
an inertial measurement unit
Data Source
AI summary
A mapping system, comprising an inertial measurement unit; a camera unit; a laser scanning unit; and a computing system in communication with the inertial measurement unit, the camera unit, and the laser scanning unit, wherein the computing system computes first measurement predictions based on inertial measurement data from the inertial measurement unit at a first frequency, second measurement predictions based on the first measurement predictions and visual measurement data from the camera unit at a second frequency and third measurement predictions based on the second measurement predictions and laser ranging data from the laser scanning unit at a third frequency.