Mapping for autonomous robotic devices
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Solution Overview
Problem
Autonomous devices, such as mobile indoor cleaning robots, face challenges in detecting transparent or reflective surfaces and narrow obstacles due to limitations in existing lidar and vision sensors, which hinder accurate mapping and navigation.
Innovation Solution
An autonomous device equipped with a radar, processor, and memory that processes raw radar data to generate a reprojection map, using Doppler speed calculations and odometry data to improve mapping accuracy, especially for obstacles difficult to detect by lidar and vision sensors, by combining radar data with existing maps.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If lidar and vision sensors are used for obstacle detection, then the device can detect obstacles in most conditions, but it fails to detect transparent or reflective surfaces and narrow chair legs
Solution Approach 1:
The patent combines radar sensing with existing lidar and vision sensors to create a multi-sensor system. The radar sensor detects obstacles through transparent or reflective surfaces that are invisible to optical sensors, while the processed radar data is integrated with map data from other sensors to provide comprehensive obstacle detection and navigation
2Measurement precision
If conventional mapping methods are used, then the device can generate basic maps, but the maps lack accuracy for optically reflective or transparent obstacles
Solution Approach 1:
The patent uses radar as an intermediary sensor that can detect obstacles through materials that are transparent or reflective to optical sensors. The radar data serves as a mediator to fill information gaps in the environmental map, particularly for obstacles that cannot be detected by conventional lidar and vision systems
3Measurement precision
If radar data is processed with smoothing and peak approximation, then noise is reduced and peaks are enhanced, but processing complexity increases
Solution Approach 1:
The patent applies Gaussian smoothing to the raw radar data, which transforms the data by convolving it with a Gaussian kernel. This parameter-based transformation enhances peak detection accuracy by reducing noise while maintaining a relatively simple computational approach that can be efficiently implemented in real-time processing
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
Enhances navigation and mapping accuracy by effectively detecting and recognizing optically reflective or transparent obstacles, leading to a more comprehensive and accurate representation of the environment.
Implementation Method 1
a radar, a processor and a memory to receive raw radar data from the radar
Implementation Method 2
receive an echo. Based on the echo raw radar data is derived
Implementation Method 3
determine a a Doppler speed of the target corresponding to the target peak
Data Source
Figure 1A~1C
Figure 2~3
Figure 4A~4D
AI summary
Data from a radar sensor (502) moving through a static environment may be smoothed and used to generate range profiles by approximating peaks. A direction of arrival (DOA) can then be determined based on the range profile in order to generate a reprojection map. The reprojection map is used to provide updates to a stored map in a robot (102).