Occupancy Grid Mapping for Absolute Vehicle Object Velocity
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing algorithms for determining absolute object velocities from vehicle sensor data are computationally complex and fail to reliably differentiate between static and dynamic objects, leading to inefficient motion detection.
Innovation Solution
A computer-implemented method that defines a grid in the earth frame of reference, updates occupancy values based on sensor data, and calculates absolute object velocities using a motion detection algorithm, thereby simplifying the process and improving accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing algorithms are used to determine absolute object velocities from vehicle sensor data, then measurement precision is improved, but device complexity increases significantly
Solution Approach 1:
The patent introduces an intermediary coordinate transformation step that converts sensor data from the vehicle's moving frame of reference to the earth's fixed frame of reference. This intermediary transformation simplifies the velocity calculation by eliminating the need for complex differential analysis, thereby reducing algorithmic complexity while maintaining measurement precision.
Solution Approach 2:
Instead of directly calculating absolute velocities from relative sensor data through complex algorithms, the patent inverts the approach by first transforming positions to the earth frame of reference and then deriving velocities from these transformed positions. This inversion simplifies the computational process while preserving measurement accuracy.
2Device complexity
If simple motion detection algorithms are used to measure occupancy change, then device complexity is reduced, but measurement precision deteriorates because static objects cannot be differentiated from dynamic objects
Solution Approach 1:
The patent uses the earth frame of reference as an intermediary that enables simple motion detection algorithms to achieve precise object differentiation. By transforming all position data to this fixed reference frame before velocity calculation, even simple algorithms can distinguish static objects (zero velocity in earth frame) from dynamic objects (non-zero velocity in earth frame), resolving the contradiction between simplicity and precision.
3Ease of operation
If the vehicle frame of reference is used for occupancy grid mapping, then ease of operation is improved, but measurement precision of absolute velocities deteriorates
Solution Approach 1:
The patent applies preliminary coordinate transformation action by converting sensor data from the vehicle frame to the earth frame before velocity calculation. This preliminary transformation maintains the ease of operation in processing occupancy grids while ensuring that subsequent velocity measurements are precise and referential to the fixed earth frame, thereby resolving the contradiction between operational ease and measurement precision.
Data Source
Figure 1A~1b
Figure 2~3
Figure 4
AI summary
Provided is a computer-implemented method for mapping a vehicle environment, the method comprising: a) defining a first grid in the earth frame of reference having a first coordinate system; b) initialising a position of the vehicle in the first grid and a first set of cell occupancy values of the first grid; c) receiving sensor data of the surroundings of the vehicle from one or more sensors on the vehicle; d) updating the first grid with a second set of occupancy values calculated at least in part from the sensor data; and e) calculating one or more absolute velocities of one or more objects in the earth frame of reference from the change in cell occupancy values of the first grid.