Occupancy Grid Map Generation Using Laser Scanner Feedback
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Solution Overview
Problem
Existing systems face challenges in generating an occupancy grid map in a sensor-centered coordinate system using a laser scanner, particularly in compensating for displacement losses and distinguishing between static and dynamic objects, leading to inconsistencies and discretization errors.
Innovation Solution
A system and method that utilize a laser scanner to create an occupancy grid map by stochastically combining current and predicted maps, incorporating sensor movement data, including speed and yaw information, to recursively update the map and determine object staticity, thereby addressing displacement losses and inconsistencies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a laser scanner is used to generate occupancy grid map in sensor-centered coordinate system, then distance measurement capability is improved, but displacement loss and map inconsistency occur
Solution Approach 1:
The system uses feedback from sensor movement data (speed and yaw information) to continuously update and correct the occupancy grid map. The mapping unit receives feedback about sensor displacement and uses this information to adjust the map coordinates, ensuring consistency between the map and actual sensor position changes.
Solution Approach 2:
The patent introduces sensor-centered coordinate system as an intermediary reference frame between the laser scanner measurements and the occupancy grid map. This intermediate coordinate system allows proper transformation of measurement data while accounting for sensor movement, preventing direct coordinate conflicts that cause map inconsistency.
2Loss of time
If occupancy grid map is updated recursively using scan data, then map freshness is improved, but discretization error increases
Solution Approach 1:
The system performs preliminary calculation of sensor displacement using speed and yaw data before updating the occupancy grid map. By pre-computing the transformation parameters based on sensor movement, the system prepares accurate coordinate transformations in advance, reducing discretization errors that would occur during the actual map update process.
3Measurement precision
If static and dynamic objects are distinguished using mapping algorithm, then object classification accuracy is improved, but computational complexity increases
Solution Approach 1:
The system applies different processing strategies to different regions of the occupancy grid map based on object characteristics. Static objects and dynamic objects are handled with different update rules and validation criteria, allowing accurate classification while optimizing computational resources by applying complex algorithms only where necessary rather than uniformly across the entire map.
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 solution effectively generates a stable occupancy grid map in a sensor-centered coordinate system, reducing discretization errors and enabling accurate differentiation between static and dynamic objects, while maintaining map consistency through hourly updates.
Implementation Method 1
it is possible to calculate the distance to the target by measuring the arrival time of a reflected wave reflected from the target
Implementation Method 2
since a ray of a laser is focused, the laser may illuminate only a specific target
Data Source
AI summary
A system for writing an occupancy grid map of a sensor centered coordinate system using a laser scanner includes a data unit that includes scan data read by the laser scanner, a past measurement map, and data relating to a movement of the sensor, a mapping unit for stochastically combining a current measurement map written from the scan data with a predicted map written by using the past measurement map and the data relating to a movement of the sensor, and a static and dynamic object detection unit for determining whether an object in the occupancy grid map is a static or a dynamic object by using a mapping algorithm of the mapping unit.