Depth-Dependent Pixel Filtering for Ambiguous ToF Returns
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Solution Overview
Problem
Time-of-flight sensors are unreliable in environments with varied lighting and multiple objects at different distances, leading to ambiguous returns and inaccurate depth measurements, which complicates object identification and obstacle avoidance in autonomous vehicles.
Innovation Solution
The use of multiple modulation frequencies for time-of-flight sensors to improve depth measurement accuracy, where a lower frequency provides a larger nominal maximum range but lower accuracy, and a higher frequency offers shorter range with higher accuracy, allowing for disambiguation of depth measurements and filtering of pixels based on intensity thresholds.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a single modulation frequency is used for time-of-flight sensors, then the device complexity is reduced, but the measurement precision and reliability deteriorate in environments with multiple objects at different distances
Solution Approach 1:
The patent divides the measurement task into multiple frequency segments, using a first modulation frequency for distant objects and a second modulation frequency for closer objects. This segmentation allows the system to resolve depth ambiguities that would be impossible with a single frequency, thereby improving measurement precision without requiring a completely complex multi-sensor system.
Solution Approach 2:
The patent dynamically switches between different modulation frequencies based on the detected scene characteristics. When multiple objects at different distances are detected, the system adapts by using appropriate frequency combinations, making the measurement process dynamic and responsive to environmental conditions rather than static and fixed.
2Measurement precision
If multiple modulation frequencies are used to improve depth measurement accuracy, then the measurement precision improves, but the device complexity and processing time increase
Solution Approach 1:
The patent segments the frequency usage based on object distance, assigning different modulation frequencies to different depth ranges. This segmentation strategy improves measurement precision for multi-object scenes while avoiding the unnecessary complexity of using all possible frequency combinations simultaneously, as each frequency segment serves a specific depth range.
Solution Approach 2:
The patent changes the modulation frequency parameter dynamically based on the detection scenario. By adjusting this key parameter according to the number and distribution of objects in the scene, the system achieves high measurement precision without permanently increasing device complexity, as the complexity only arises when and where needed.
3Reliability
If multiple modulation frequencies are used to disambiguate depth measurements, then the reliability of sensor data improves, but the processing time increases
Solution Approach 1:
The patent performs preliminary analysis of the sensor data to identify scenes with multiple objects at different distances before applying multi-frequency disambiguation. This preliminary action allows the system to apply the time-consuming multi-frequency processing only when necessary, thereby improving reliability for critical cases while minimizing overall processing time by avoiding unnecessary complex processing in simple scenes.
Solution Approach 2:
The patent dynamically changes the processing approach based on scene parameters. When the scene is determined to be simple (single object or uniform distance), the system uses faster single-frequency processing. When complexity is detected (multiple objects at different distances), it switches to multi-frequency disambiguation, thus optimizing the balance between reliability and processing time by adapting to the actual scene requirements.
4Measurement precision
If depth-dependent filtering is applied to improve object identification accuracy, then the measurement precision improves, but the device complexity increases
Solution Approach 1:
The patent applies different filtering criteria to different depth regions in the sensor data. By making the filtering quality local rather than uniform, the system improves object identification accuracy for specific depth ranges where it matters most, while avoiding the complexity of applying complex filters uniformly across the entire scene. This local quality approach optimizes the balance between precision and processing complexity.
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 enhances the reliability and accuracy of sensor data, enabling more precise object detection and safer navigation in complex environments by disambiguating depth measurements and filtering out less relevant data, thus improving the overall performance of autonomous vehicles.
Implementation Method 1
Time-of-flight sensors may be unreliable in certain environments
Data Source
AI summary
Sensors, including time-of-flight sensors, may be used to detect objects in an environment. In an example, a vehicle may include a time-of-flight sensor that images objects around the vehicle, e.g., so the vehicle can navigate relative to the objects. Sensor data generated by the time-of-flight sensor can include returns associated with highly reflective objects that cause glare. In some examples, a depth of a sensed surface is determined from the sensor data and additional pixels at the same depth are identified. The subset of pixels at the depth are filtered by comparing a measured intensity value to a threshold intensity value for the depth. Other threshold intensity values can be applied to subsets of pixels at different depths.