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The storage shelves are available in different sizes for storing objects in different shapes, colours, sizes and surfaces. Many technologies are available for transforming human-intensive jobs into machines, such as robots handling picking, placing and sorting, but these robots need datasets to process and handle effectively. A distance sensor is suitable to measure distances between objects and to obtain free spaces that are available in a shelf tier. A Light Detection and Ranging (LiDAR) sensor can measure distances better than Ultrasonic (US) and Infrared distance sensors (IR) because it has good sensitivity to detect near and far objects. Spherical coordinates that are recorded from a LiDAR are transformed into cartesian coordinates to obtain a 3D impression of a shelf tier. This study aims to propose an effective 3D sensory scanning system to estimate the percentage of free space in a shelf tier by using cloud point data (CPD). The first step is to determine a suitable distance sensor that can be mounted on a tripod scanner to obtain CPD. The spherical datasets then were converted into cartesian coordinates. The motor speed was analysed with Pearson Correlation analysis to determine a suitable rotating speed to construct a 3D impression. Experimental results showed the designed scanner is capable to scan CPD at a suitable motor rotational speed, and with the 3D plot from cartesian coordinates, it is easy to distinguish between areas with and without objects in a shelf tier.
Ramachandran et al. (Mon,) studied this question.