Accurately estimating required storage space is a persistent challenge for individuals and organizations looking to rent storage units or organize confined spaces. Users frequently rely on rough estimation or visual judgment techniques, which can lead to overestimating space and incurring unnecessary costs. On the other hand, underestimating space can lead to issues of repacking, reallocation, or inefficient use of storage. This capstone project aims to address the issue by developing, testing, and validating a LiDAR spatial measurement system, specifically designed to assist users in objectively estimating storage requirements within a controlled environment. In creating such a system, the user needs to be placed at the forefront of the project, making the process as convenient as possible.
Furthermore, the objective of this project is to design and evaluate a three-axis dimensional measurement framework using 2D LiDAR technology to estimate the physical dimensions and volume of boxed items placed within a defined space. The system utilizes a SICK TiM561 LiDAR sensor, which provides a planar range and angled measurements. Combining this sensor's capabilities with the application of a phased measurement approach can be used to overcome the inherent limitation of 2D LiDAR in capturing three-dimensional information. By sequentially measuring object width, height, and depth along their respective axes, the system computes an estimate of volume that can be used to assess packing efficiency and space utilization.
The project is structured into four development phases. Phase A focuses on measuring box width along the horizontal (X) axis using a single LiDAR scan and geometric analysis of detected object edges. Phase B introduces controlled vertical motion of the LiDAR sensor to measure box height along the Y axis. Phase C extends the approach to measure box depth along the Z axis by reorienting the sensor to capture side-profile scans. Phase D integrates measurements from all three axes to estimate object volume and evaluate cumulative system accuracy. At each phase, measurement accuracy and repeatability are assessed before advancing to subsequent stages.
Current storage estimation methods often rely on manual measurements and rough visual judgment, which can result in inaccurate storage planning and wasted space. This capstone project addresses that issue by developing a 2D LiDAR based scanning system capable of measuring object dimensions in the X, Y, and Z axes. Measurements are then converted into a digital model that supports storage capacity estimation and packing efficiency analysis.