Radar Odometry for Vehicle Navigation
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Solution Overview
Problem
Existing odometry methods for vehicles, such as wheel odometry and IMU, suffer from limited accuracy due to tire uncertainties and measurement errors, while GPS and LIDAR have limitations in 2D dynamic variable estimation and weather conditions, respectively, and are insufficient for 3D navigation.
Innovation Solution
The use of real-time radar detection and ranging (RADAR) to measure stationary objects around a moving vehicle, employing Doppler radars for higher accuracy and implementing a radar odometry method that calculates linear and angular velocities through a least squares problem, with noise cancellation and data filtering using the RANSAC algorithm to differentiate stationary and moving objects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If wheel odometry is used for estimating linear velocities, then the odometry can be achieved using simple sensors, but the accuracy is limited due to tire size uncertainties and low encoder resolution
Solution Approach 1:
The patent replaces the mechanical wheel encoder system with a radar-based measurement system. Instead of using wheel encoders that measure rotation mechanically, the system uses radar to directly measure the relative velocity between the vehicle and stationary objects, eliminating the need for precise tire size and encoder resolution requirements.
Solution Approach 2:
The patent introduces radar as an intermediary measurement device that indirectly measures vehicle velocity by measuring the relative velocity between the vehicle and stationary objects in the environment. This intermediary approach bypasses the limitations of direct wheel measurement.
2Measurement precision
If IMU is used for measuring angular velocities, then real-time measurement is achieved, but measurement errors accumulate over time causing integration drift
Solution Approach 1:
The patent uses radar measurements of stationary objects to provide feedback that corrects the integration drift of IMU measurements. By continuously comparing radar-derived velocity with IMU-derived velocity, the system can identify and correct accumulated errors in real-time.
Solution Approach 2:
The patent merges IMU measurements with radar measurements to create a hybrid odometry system. The IMU provides high-frequency angular velocity data while the radar provides absolute reference measurements, combining the advantages of both sensors to eliminate their respective weaknesses.
3Measurement precision
If GPS is used for odometry, then position information can be obtained, but it does not function in places with limited satellite reception such as tunnels
Solution Approach 1:
The patent makes the odometry system self-sufficient by using onboard radar to measure environmental objects directly, eliminating dependence on external satellite infrastructure. The system uses its own radar emissions to detect and measure stationary objects, providing autonomous positioning capability independent of GPS.
4Measurement precision
If LIDAR is used for odometry, then 3D measurement capability is achieved, but it does not work in all weather conditions
Solution Approach 1:
The patent uses radar waves that can penetrate through weather conditions like rain, fog, and dust, replacing LIDAR's optical photons that are easily scattered or absorbed by atmospheric particles. Radar waves are more robust in adverse weather, providing reliable measurement when LIDAR fails.
5Measurement precision
If visual odometry is used via cameras, then odometry can be achieved using optical sensors, but it suffers from integration drift problem causing random unbounded drift in calculated navigation position
Solution Approach 1:
The patent replaces the visual optical measurement system with a radar-based measurement system. Instead of using cameras that capture optical images for feature tracking, the system uses radar to directly measure relative velocity with stationary objects, providing more reliable and drift-free measurements.
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
This approach provides accurate 2D and 3D odometry estimates, enhancing navigation and control of vehicles by reducing measurement errors and overcoming environmental limitations, with the ability to calculate dynamic variables in real-time for both 2D and 3D environments.
Implementation Method 1
employing Doppler radars for higher accuracy
Data Source
AI summary
Techniques and examples pertaining to vehicle odometry using one or more radars disposed on a vehicle are described. A method for radar odometry may involve receiving, by a processor from the radars, measurement data of stationary objects and moving objects that are located in an environment a vehicle is traversing. The method may also involve performing, by the processor, a random sample consensus (RANSAC) calculation to select the measurement data of the stationary objects and disregard the measurement data of the moving objects. The method may also involve calculating, by the processor, one or more dynamic variables of the vehicle based on the measurement data of the stationary objects. The proposed method processes the measurement data of the stationary objects with one single RANSAC calculation and one least squares problem solving, thereby greatly reducing computation cost and time as well as latency in operation for providing the vehicle odometry.