Lidar Data Merging for Retail Shelf Scanning
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Solution Overview
Problem
Current methods for out-of-stock detection, low stock detection, price label verification, and planogram compliance in retail environments are labor-intensive and error-prone, relying on human associates for physical checks and are not efficiently addressed by existing lidar sensor systems due to vertical scanning limitations and data integration challenges.
Innovation Solution
A mobile automation system equipped with multiple lidar devices scanning at non-zero and non-perpendicular angles to the movement direction, combining point cloud data into a common set, binning points into planes perpendicular to the movement direction, ignoring outlier points, and performing curve fitting to smooth noise, producing a virtual lidar scan for improved data integration and feature detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of stationary object
If multiple lidar sensors are arranged vertically to maximize vertical coverage, then the vertical coverage of shelves is improved, but the difficulty of combining lidar sensor data increases due to different shadows from each sensor
Solution Approach 1:
The patent merges data from multiple vertically arranged lidar sensors into a unified representation. The processor combines the individual scan data sets from each lidar sensor, treating them as complementary views of the same shelf space. This merging process resolves the data integration complexity by creating a single consolidated data structure that captures all vertical coverage information without requiring separate processing of each sensor's shadowed regions.
2Area of stationary object
If lidar sensors scan in a vertical direction, then the vertical coverage is maximized, but gaps between stock items may not be detected depending on the speed of the mobile automation apparatus
Solution Approach 1:
The patent introduces angular scanning capability that scans at non-zero and non-perpendicular angles to the movement direction of the mobile automation apparatus. This angular dimension complements the vertical scanning, creating oblique scan paths that can detect gaps between items that vertical scans miss. The combination of vertical and angular scanning dimensions ensures comprehensive detection of stock gaps regardless of the apparatus's movement speed.
3Measurement precision
If human associates manually check shelves for issues, then the detection of stock issues is achieved, but the labor intensity and error rate increase significantly
Solution Approach 1:
The patent replaces the mechanical human inspection process with an automated optical scanning system. Multiple lidar sensors mounted on a mobile automation apparatus automatically scan shelves, capturing three-dimensional spatial data and detecting stock issues such as gaps, low stock, and planogram compliance violations. This mechanical-to-automated substitution eliminates human labor intensity and error-prone manual checking while maintaining high detection accuracy through precise optical measurement.
4Loss of time
If data from manual checks is processed manually before resolution, then the detection process is completed, but the time delay increases leading to lost sales
Solution Approach 1:
The system performs self-service through automated data processing. The mobile automation apparatus not only collects scan data but also automatically processes the point cloud data to generate actionable insights. The processor immediately analyzes the scanned data, identifies stock issues, and can trigger restocking alerts or notifications without requiring manual data entry or processing. This end-to-end automation eliminates time delays between detection and resolution while maintaining reliable issue identification.
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 enables efficient and accurate detection of stock levels and compliance issues, reducing manual processing time and improving the timeliness of inventory management, thereby minimizing lost sales and customer dissatisfaction.
Implementation Method 1
receive, via the communication interface, point cloud data representing respective angular lidar scans of a region as at least two lidar devices are moved relative the region
Data Source
AI summary
A device and method for merging lidar data is provided. Point cloud data is combined, via a lidar imaging controller, into a common point cloud data set, each set of point cloud data representing respective angular lidar scans of a region as at least two lidar devices are moved relative to the region of a shelf. The respective angular lidar scans from each lidar device occur at a non-zero and non-perpendicular angle to a movement direction. Common point cloud data set points are binned into a plane perpendicular to the movement direction of a mobile automation apparatus and extending from a virtual lidar position. The lidar imaging controller combines points among multiple planes.