LiDAR Camera Fusion for Anti-Collision Control
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Solution Overview
Problem
Current anti-collision and motion control systems lack effective integration of LiDAR and high-fidelity camera techniques for accurate object detection and control in dynamic environments, leading to inefficiencies in monitoring and managing assets in real-time, particularly in complex physical settings like industrial environments.
Innovation Solution
An integrated anti-collision and motion control system utilizing LiDAR and high-fidelity camera modules, along with processing circuitry and a central coordinator, to detect and classify objects, track movements, and control operating parameters, enabling intelligent and safe asset management through a mesh network that minimizes data transmission and relies on plug-and-play deployment of monitoring devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If LiDAR and camera systems are integrated for accurate object detection, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent combines LiDAR and camera systems into an integrated monitoring device that simultaneously performs depth measurement and visual detection. The processing circuitry coordinates both sensors to detect objects and determine collision risks, achieving high measurement precision through multi-sensor fusion while managing complexity through unified system architecture.
Solution Approach 2:
The integrated system performs multiple functions including object detection, depth measurement, motion tracking, and collision risk assessment using a single coordinated device. This multi-functionality improves detection accuracy across various parameters while reducing the need for separate specialized devices.
2Productivity
If real-time monitoring of multiple objects is implemented, then productivity is improved, but use of energy increases
Solution Approach 1:
The system processes data selectively by focusing computational resources on detected objects and potential collision risks rather than continuously processing all sensor data at full resolution. The processing circuitry adjusts monitoring intensity based on environmental conditions and object priority, maintaining high productivity while reducing unnecessary energy consumption.
Solution Approach 2:
The monitoring system operates in periodic cycles, alternating between active sensing phases and processing phases. The LiDAR and camera systems capture data in intervals, and the processing circuitry analyzes accumulated data to determine collision risks, enabling real-time monitoring capability while managing energy consumption through rhythmic operation patterns.
3Reliability
If comprehensive data processing is performed locally, then reliability is improved, but device complexity increases
Solution Approach 1:
The system divides processing tasks between the processing circuitry in the monitoring device and a central coordinator. The local processing circuitry handles immediate sensor data processing and collision detection, while more complex data analysis and control decisions are managed by the central coordinator. This segmentation improves reliability through distributed processing while managing device complexity by separating computational loads.
Solution Approach 2:
The processing circuitry acts as an intermediary between the sensors and the central coordinator, performing preliminary data processing and filtering before transmitting information to the central system. This intermediate processing layer improves reliability by ensuring data quality at the source while reducing the complexity burden on any single component.
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
The system provides robust, real-time monitoring and control of assets, enhancing safety and operational transparency by accurately identifying and managing various objects in physical environments, reducing data transmission requirements and allowing for wireless operation with minimal infrastructure.
Implementation Method 1
one or more light detection and ranging (LiDAR) systems configured to detect locations of one or more objects in an environment
Data Source
AI summary
Systems and methods presented herein include an anti-collision and motion monitoring system includes one or more light detection and ranging (LiDAR) systems configured to detect locations of one or more objects in an environment. The anti-collision and motion monitoring system also includes one or more camera systems configured to capture images of the one or more objects in the environment that are detected by the one or more LiDAR systems. The anti-collision and motion monitoring system further includes processing circuitry configured to coordinate operation of the one or more LiDAR systems and the one or more camera systems, to receive inputs from the one or more LiDAR systems and the one or more camera systems relating to the one or more objects in the environment, to process the inputs received from the one or more LiDAR systems and the one or more camera systems to determine outputs relating to control of at least one of the one or more objects in the environment, and to communicate the outputs to a central coordinator to control one or more operating parameters of at least one of the one or more objects in the environment based at least in part on the inputs received from the one or more LiDAR systems and the one or more camera systems.