Radar-Inertial SLAM for Vehicle Positioning in Poor Visibility
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Solution Overview
Problem
Existing SLAM algorithms for autonomous vehicles struggle to create accurate maps and locate vehicles precisely in real-time under poor visual conditions without relying on external satellite systems.
Innovation Solution
A method utilizing a Doppler radar system and inertial navigation system to acquire and process point data, filter out moving objects, and execute a SLAM algorithm to achieve precise localization and mapping, integrating the data to correct inertial navigation system drift.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a SLAM algorithm is implemented using only visual detection devices (Lidar, cameras) for autonomous navigation, then the vehicle can build and improve a map of the environment, but the system fails to provide accurate positioning under poor visual conditions (rain, fog, darkness, dust)
Solution Approach 1:
The patent combines Doppler radar system and inertial navigation system outputs to provide reliable positioning information under all visual conditions. The radar system detects points with radial speeds while the inertial system provides attitude and position data, and their integration through a SLAM algorithm ensures accurate localization regardless of weather or lighting conditions.
Solution Approach 2:
The patent introduces a mediator system that processes and integrates data from both the Doppler radar system and inertial navigation system. This intermediary processing layer reconciles the measurements from both systems, using the radar's all-weather capability to compensate for the inertial system's drift while maintaining continuous positioning accuracy.
2Reliability
If a Doppler radar system and inertial navigation system are integrated to achieve positioning under poor visual conditions, then reliability improves, but the device complexity increases
Solution Approach 1:
The patent makes the calculator perform multiple functions: it processes Doppler radar data to detect environmental points, processes inertial navigation data to obtain attitude and position, executes the SLAM algorithm for mapping and localization, and integrates all these functions into a single on-board system, thereby reducing overall system complexity despite the multiple sensors involved.
Solution Approach 2:
The system uses the vehicle's own movement model, informed by the inertial navigation system outputs, to relocate detected points from a moving reference point to a terrain reference point. This self-referential approach allows the system to maintain accurate positioning without requiring external reference systems, simplifying the overall architecture.
3Measurement precision
If visual detection devices are used for environment mapping, then the map accuracy is sufficient for navigation, but the system cannot operate autonomously in poor visual conditions without external satellite positioning
Solution Approach 1:
The patent replaces the optical/mechanical detection system (Lidar, cameras) with a Doppler radar system for the primary positioning function. The radar system uses electromagnetic wave Doppler shifts to detect radial speeds of environmental points, providing all-weather capability that substitutes for the weather-dependent visual systems while maintaining mapping and localization functions.
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
Enables accurate mapping and localization of vehicles in various visual conditions, reducing inertial navigation system drift by integrating radar and inertial data, enhancing precision and accuracy.
Implementation Method 1
acquiring a set of points at the current time delivered by the Doppler radar system, one point of the set of points being characterized by a position and a radial speed
Implementation Method 2
acquiring the attitude of the vehicle at the current time, delivered by the inertial navigation system
Data Source
AI summary
A method carried out by an on-board calculator on a vehicle, including acquiring a set of points at the current time, delivered by a Doppler radar system of the vehicle, acquiring the attitude of the vehicle at the current time, delivered by a vehicle inertial navigation system, orienting the set of points at the current time with respect to a land reference frame taking into account the attitude at the current time, processing the radial speeds of the points to calculate an estimated speed of the vehicle at the current time, calculating a position of the vehicle at the current time from the estimated speed at the current time, and executing a simultaneous localization and mapping algorithm based on the position of the vehicle at the current time over a plurality of successive times.