LiDAR Bracket and Control Unit for False Detection Reduction
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Solution Overview
Problem
Existing LiDAR systems on work machines often produce false object detections due to reflective surfaces on the machine itself, leading to inaccurate readings and increased costs with existing solutions that require extensive resources and complex algorithms.
Innovation Solution
A LiDAR system configured to exclude laser beams emitted towards the work machine by using an angled or blinder bracket to shift the field of view and a control unit that filters out data samples corresponding to excluded coordinates, thereby reducing false object detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a LiDAR system is installed on a work machine with reflective surfaces, then the machine can detect terrain and obstacles, but the reflective surfaces cause false object detections
Solution Approach 1:
The patent extracts and removes the harmful effect of reflective surfaces by identifying and excluding beam coordinates that would reflect off the work machine's body. The control unit filters out data samples corresponding to excluded coordinates, effectively separating the harmful reflected beams from the useful detection beams.
Solution Approach 2:
The patent applies preliminary action by pre-calculating and storing a database of excluded coordinates before the LiDAR begins operation. The control unit uses this pre-computed database to filter out beams that would reflect off the work machine, preventing false detections before they occur rather than correcting them afterward.
2Measurement precision
If existing solutions use machine learning models with extensive sensors to identify false positives, then detection accuracy may improve, but implementation cost and system complexity increase
Solution Approach 1:
The patent extracts only the essential information needed to solve the problem - the beam coordinates and their reflection paths - rather than using extensive sensor data and complex machine learning models. This extraction approach achieves accurate false positive identification with minimal system complexity.
Solution Approach 2:
The patent replaces expensive, complex machine learning systems with a simple, computationally efficient coordinate filtering approach. The solution uses basic geometric calculations and database lookups instead of resource-intensive neural networks, achieving the same goal with much lower implementation cost and 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
The solution effectively reduces false object detections by avoiding reflective surfaces on the work machine, improving accuracy and reducing implementation costs, while maintaining operational efficiency and safety.
Implementation Method 1
the measurement of a time of flight (ToF) for each emitted beam to reflect off an opposing surface (or fail to reflect) and return to the LiDAR sensor
Implementation Method 2
a laser beam encountering a reflective surface, such as a glass window of a cabin or a mirror, may strike the reflective surface and deflect from its original trajectory
Data Source
AI summary
A work machine, light detection and ranging (LiDAR) system, and method of reducing false object detections are disclosed. The work machine may include a frame, a power unit, a locomotive device, and the LiDAR system. The LiDAR system may include at least one laser, at least one sensor, a bracket, and a control unit configured to execute an object detection program. The LiDAR system is configured to exclude from its object detection program a plurality of beams emitted toward the work machine through a design of the bracket and/or software methods of the control unit. The method may include storing a database of exclude coordinates and using the database to filter out undesirable data samples from the object detection program.