Camera-Based Localization Maps with Sensor Reflection Landmarks
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Solution Overview
Problem
Conventional systems that generate camera-based maps require significant computing resources due to the amount of sensor data processing, and using only image data for localization can lead to reduced precision or accuracy, especially when compared to RADAR or LiDAR data.
Innovation Solution
Augmenting camera-based maps with sensor reflection information, such as RADAR or LiDAR data, to enhance localization accuracy while reducing computational requirements by encoding this data as attributes within the camera-based map.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If camera-based maps are generated using only image data, then computing resources required are reduced, but localization precision and accuracy deteriorate
Solution Approach 1:
The patent merges camera-based maps with RADAR or LiDAR data by integrating multiple sensor modalities into a unified map representation. This combination allows the system to maintain reduced computational requirements while achieving improved localization precision through multi-sensor fusion, directly resolving the contradiction between resource efficiency and measurement accuracy.
Solution Approach 2:
The patent creates composite map data by combining information from different sensor types (camera, RADAR, LiDAR) into a unified structure. This composite approach leverages the strengths of each sensor modality—visual features from cameras and spatial/reflection data from RADAR or LiDAR—to achieve superior localization accuracy without proportionally increasing computational burden.
2Measurement precision
If multiple types of sensor data are processed to generate map layers, then localization accuracy is improved, but computing resources and data communication requirements increase
Solution Approach 1:
The patent extracts and integrates only the essential features from multiple sensor data types into a unified map representation, rather than processing and communicating all raw sensor data. This selective extraction approach maintains high localization accuracy by preserving critical spatial and reflective features while significantly reducing computational complexity and data communication requirements.
3Measurement precision
If RADAR or LiDAR data is used for localization, then feature extraction accuracy is improved, but the system cannot detect landmarks that are not associated with sensor reflections
Solution Approach 1:
The patent creates a universal map representation that can be used by multiple sensor types (camera, RADAR, LiDAR) for localization. This multi-functional map structure enables the system to detect both reflective landmarks (via RADAR/LiDAR) and non-reflective visual landmarks (via camera), thereby improving both feature extraction accuracy and overall landmark detection versatility across different sensor modalities.
Data Source
AI summary
In various examples, performing localization using camera-based maps augmented with sensor reflection information for autonomous and/or semi-autonomous systems and applications is described herein. For instance, and for a machine, an image-based map may be used to determine one or more locations associated with one or more landmarks located within the environment and also determine that the landmark(s) is associated with sensor reflections. Sensor data generated using the machine may then be analyzed with respect to the location(s) associated with the landmark(s) within the environment in order to determine a location of the machine within the environment. As described herein, various techniques may be used to localize the machine, such as using one or more distance transform areas around the landmark(s) and/or using costs determined based at least on analyzing the distance transform area(s) with respect to points represented by the secondary data.