Co-Aligned Rotating LiDAR-Imager Timing for Sharp 3D Mapping
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Solution Overview
Problem
Combining LIDAR and image sensor data to create a synchronized 3D representation of an environment is challenging due to differences in photon collection timing, fields-of-view, and exposure times, leading to issues like image smearing during rotation.
Innovation Solution
A device with a LIDAR sensor and an image sensor mounted on a rotating platform, where the image sensor captures pixels synchronously with LIDAR light pulse emission and detection times, and data is mapped to corresponding points in the point cloud, using a controller to align fields-of-view and adjust exposure times to reduce image artifacts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of stationary object
If the image sensor captures images during rotation to enable 3D mapping, then the field of view coverage is improved, but image smearing occurs due to rotation during exposure
Solution Approach 1:
The image sensor operates in periodic intervals, capturing images only at specific moments when the LIDAR sensor is not transmitting pulses or during designated exposure windows. This periodic operation allows the sensor to capture sharp images without rotation-induced smearing while still achieving comprehensive scene coverage through multiple discrete captures during the rotation cycle
Solution Approach 2:
The system pre-coordinates the exposure timing of the image sensor with the LIDAR pulse transmission schedule. Before each LIDAR pulse transmission, the image sensor is instructed to complete its exposure and capture the image, ensuring that image capture occurs during safe time windows when rotation artifacts are minimized or eliminated
2Measurement precision
If the image sensor and LIDAR sensor are co-aligned on the rotating platform, then data fusion accuracy is improved, but synchronization complexity increases
Solution Approach 1:
The image sensor and LIDAR sensor are physically co-aligned on the same rotating platform, merging their optical axes and ensuring they observe the same scene from identical viewpoints. This physical merging eliminates the need for complex post-processing alignment and simplifies the synchronization requirements to primarily temporal coordination rather than spatial calibration
Solution Approach 2:
The system implements a centralized controller that receives timing information from both sensors and coordinates their operation. The controller uses feedback from the LIDAR pulse transmission timing to adjust and synchronize the image sensor exposure timing, ensuring that both sensors operate in a coordinated manner that maximizes data fusion accuracy while managing synchronization complexity
3Measurement precision
If the image sensor exposure time is increased to improve image quality, then photon collection is improved, but motion blur increases during rotation
Solution Approach 1:
The image sensor uses short, periodic exposure intervals that are synchronized with the LIDAR pulse transmission cycle. Each exposure is brief enough to minimize motion blur from rotation, but the periodic repetition of exposures during the rotation cycle allows accumulation of multiple images that can be processed to achieve the desired image quality without requiring long individual exposure times
Solution Approach 2:
The system schedules image exposures to occur immediately before LIDAR pulse transmissions, when the platform is at known angular positions. By capturing images at these predetermined, synchronized moments, the system ensures that exposure duration can be kept short while still achieving adequate image quality through the coordinated timing and subsequent image 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
Improves sensor data quality by synchronizing data collection and reducing image smearing, enabling accurate fusion of LIDAR and image data for enhanced 3D mapping.
Implementation Method 1
a LIDAR sensor can determine distances to environmental features while scanning through a scene to assemble a 'point cloud' indicative of reflective surfaces. Individual points in the point cloud can be determined, for example, by transmitting a laser pulse and detecting a returning pulse, if any, reflected from an object in the environment
Implementation Method 2
An example image sensor can capture an image of a scene viewable to the image sensor. For instance, the image sensor may include an array of charge-coupled devices (CCDs) or other types of light sensors. Each CCD may receive a portion of light from the scene incident on the array
Data Source
AI summary
Example implementations are provided for an arrangement of co-aligned rotating sensors. One example device includes a light detection and ranging (LIDAR) transmitter that emits light pulses toward a scene according to a pointing direction of the device. The device also includes a LIDAR receiver that detects reflections of the emitted light pulses reflecting from the scene. The device also includes an image sensor that captures an image of the scene based on at least external light originating from one or more external light sources. The device also includes a platform that supports the LIDAR transmitter, the LIDAR receiver, and the image sensor in a particular relative arrangement. The device also includes an actuator that rotates the platform about an axis to adjust the pointing direction of the device.