LiDAR Waveform Design for High-Throughput Depth Sensing
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Solution Overview
Problem
Current LiDAR systems face limitations in throughput and spatial resolution due to the need for time intervals between pulses that are longer than the expected time-of-flight (ToF), which restricts their ability to operate at higher sampling rates or larger fields of view, especially in long-range applications.
Innovation Solution
The system emits succession of pulses with varying temporal spacings, using tuple or dither structures, allowing for unique identification of echoes and calculation of times of flight, even at intervals shorter than the expected ToF, thereby increasing throughput and enabling higher spatial resolution or larger fields of view.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the time interval between consecutive pulses is chosen to be longer than the time of flight, then unambiguous determination of time of flight is achieved, but the sampling rate is limited and throughput is reduced
Solution Approach 1:
The pulse train is segmented into multiple groups with different temporal spacings between pulses within each group. By dividing the pulse sequence into structured groups (e.g., tuples of pulses with specific spacing patterns), the system can identify echoes from specific pulse groups even when pulses are closely spaced, thereby enabling higher sampling rates while maintaining unambiguous ToF determination through group-based identification.
Solution Approach 2:
The system employs periodic pulse sequences with varying temporal spacings within periodic groups. Each group of pulses follows a defined spacing pattern that repeats periodically, allowing the receiver to correlate incoming echoes with known periodic patterns. This periodic structure enables the system to distinguish between echoes from different pulses even at high repetition rates, resolving the contradiction between high throughput and unambiguous ToF measurement.
2Productivity
If pulses are emitted at high sampling rates with short intervals, then throughput and spatial resolution are improved, but confusion between consecutive pulses occurs
Solution Approach 1:
Different local temporal spacing patterns are assigned to different groups of pulses within the overall pulse train. Each pulse group has a unique local spacing characteristic (e.g., different intervals between pulses within the same group), allowing the receiver to identify which specific pulse generated each echo by matching the observed echo timing pattern to the known local spacing patterns of transmitted groups.
Solution Approach 2:
The temporal spacing between pulses is dynamically varied within structured groups rather than using uniform spacing. By introducing dynamic variations in pulse intervals within each group (e.g., alternating short and long intervals in a predictable pattern), the system creates unique temporal signatures for each pulse group, enabling reliable pulse identification even at high sampling rates where pulses are closely spaced.
3Reliability
If the time interval between pulses is extended to avoid confusion, then pulse identification is reliable, but the field of view and detection capability for small objects are reduced
Solution Approach 1:
The pulse train is segmented into multiple groups with different temporal spacings between pulses within each group. By dividing the pulse sequence into structured groups (e.g., tuples of pulses with specific spacing patterns), the system can identify echoes from specific pulse groups even when pulses are closely spaced, thereby enabling higher sampling rates while maintaining unambiguous ToF determination through group-based identification.
Solution Approach 2:
The system varies the temporal spacing parameter within structured pulse groups to create unique identification patterns. By changing the time interval parameter between pulses in a controlled, patterned manner within each group, the system maintains reliable pulse identification while allowing closer pulse spacing overall, thereby expanding the effective field of view and improving detection capability for small objects that require higher sampling rates.
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 allows LiDAR systems to operate at higher sampling rates and larger fields of view, improving the quality of depth mapping by avoiding confusion between pulses and enhancing the detection of small objects and reducing the impact of stray-light interference.
Implementation Method 1
The depth value at each pixel in the depth map is derived from the difference between the emission time of the outgoing pulse and the arrival time of the reflected radiation from the corresponding point in the scene, which is referred to as the 'time of flight' of the optical pulses.
Data Source
AI summary
Depth-sensing apparatus includes a laser, which is configured to emit pulses of optical radiation toward a scene. One or more detectors are configured to receive the optical radiation that is reflected from points in the scene and to output signals indicative of respective times of arrival of the received radiation. Control and processing circuitry is coupled to drive the laser to emit a succession of output sequences of the pulses with different, respective temporal spacings between the pulses within the output sequences in the succession, and to match the times of arrival of input sequences of the signals to the temporal spacings of the output sequences in order to find respective times of flight for the points in the scene.