Morphological Study of Granular-Granular Impact Craters through Time-of-Flight Cameras: from Concept to Automation in Python

The gist The authors propose a methodology for studying impact craters based on Time-of-Flight sensors and release an open-source Python library to automate the process, which demonstrates that using a Time-of-Flight camera and automating data processing with a software library can produce very accurate results while reducing the time spent on different stages of the process

Introduction and Motivation

The study utilizes laboratory made granular-granular impact craters as model analogues of planetary impact craters This work proposes to use a Time-of-Flight camera (Microsoft Kinect One) for the acquisition of depth data They show comparisons between the typically used technique and the analysis derived from the Timeof-Flight data The results show that using a Time-of-Flight camera and automating the data processing with a software library for the systematic study of impact craters can produce very accurate results while reducing the time spent on different stages of the process

Mapping and Measurement Techniques

The paper discusses various techniques for three-dimensional measurement and reconstruction of surfaces, noting that access to this software often comes with a high financial cost Profilometry-based methods include laser profilometry, which is based on the principle of triangulation where a laser projects a beam of light onto the surface of interest and a sensor records the position and angle of the reflected beam Structured light profilometry is another technique where a pattern of structured light, such as stripes or lines, is projected onto the surface, and an image of the illuminated surface is captured

LiDAR and Time-of-Flight Principles

Light Detection and Ranging (LiDAR) technology is based on the Timeof-Flight principle, which measures the time it takes for light emitted by a device to travel to the surface of an object and return to the sensor of the unit The second generation of Microsoft Kinect (KinectToF) stands out among LiDAR devices based on the Timeof-Flight principle, as it offers several improvements over its predecessor which utilizes structured light (SL) method for depth information acquisition It has been determined that the second generation, utilizing ToF technology, is superior

Materials and Experimental Setup

An experimental system was designed to recreate the formation of impact craters by using a KinectToF sensor for the data acquisition The experiment involved constructing a square-based sandbox with dimensions of 45 cm per side and 15 cm in height as the surface or granular bed in which the crater forms after the impact of a sand lump projectile Impacts were carried out by releasing a granular projectile from heights ranging from 0.1 m to 20 m, respectively

Morphological Observables and Software

Craters can be classified into two groups: simple and complex craters The paper defines several morphological observables, such as the height of the central peak Hcp which can be defined as the difference between its maximum depth value dcp and the maximum observable depth dmax The authors developed craterslab, a software library that is able to automate data acquisition from Time-of-Flight sensors and process the data to retrieve the main crater morphologic features The workflow of the software can be summarized in fetching surface mapping data directly from sensors or locally stored files, classifying the surface based on the observed formation, computing the shape of the crater by fitting an ellipse to the crater rim, determining morphological crater features and visualizing the results

Results and Validation

The study conducted a set of experiments at different launching heights on a compacted or loose packed sand bed as described in section 3, producing a wide range of impact craters types or sand mounds Figure 5 shows the outcomes of three different type of morphologies produced experimentally When comparing the images in the first and second rows of Figure 5, the remarkable similarities between the experimental craters and their three-dimensional visualizations by craterslab are evident In comparison to laser profilometry, Kinect depth data exhibits an offset of ±1 mm in our measurements As a result, Kinect provides higher precision in the threedimensional reconstruction of granular impact surfaces

Conclusion

The authors propose a methodology for studying impact craters based on Time-of-Flight sensors KinectToF exhibits high precision for the task, outperforming laser profilometry The software is designed to simplify the data acquisition and analysis process, allowing researchers to focus on the interpretation of results rather than spending time on data processing The automatic computation of other observables performed by the software, such as excess volume, excavated volume and inner slope of the crater, may be pivotal for advancing various research topics in field This automation not only saves time but also ensures a more reliable and consistent analysis The library is open source and packaged for distribution via pypi Code availability section Name of the library: craterslab Contact: gustavo.vieralopez@gssi.it Hardware requirements: Any system compatible with Windows, Linux or MacOS Program language: Python 3.10+ Software required: Python The source code is available for downloading at the link https://github.com/gvieralopez/craterslab Data availability The data used for this work is available for downloading at the link https://github.

1. Bold header: Automation of Crater Morphological Feature Measurement

This improvement enables AI systems to automatically measure the main crater morphologic features, such as shape, depth, local slope, excess and excavated volumes as well as central peak volume and height using the craterslab software library.

2. Bold header: High-Precision 3D Reconstruction for Granular Craters

AI systems can utilize Time-of-Flight (ToF) data from sensors like the Microsoft Kinect to produce very accurate results from craters morphology, specifically achieving a resolution equivalent to profilometry in the X, Y plane with a standard deviation of 0.028 mm.

3. Bold header: Automated Ellipse Fitting and Diameter Calculation

The system can be improved to automate the determination of crater dimensions by using the software's capability to automatically fitted a rotated ellipse using our software in order to compare both methods for measuring the diameter of the crater.

4. Bold header: Numerical Integration for Volume Calculation

AI systems can move beyond geometric approximations by employing numerical integration, allowing them to determine variables like cavity volume with greater precision than traditional methods.

5. Bold header: Cross-Correlation with Natural Analogues

The system can be enhanced to perform automated comparisons between experimental results and natural geological features, as demonstrated by the software's ability to compare lunar crater observables with those from platforms like LROC.

Laboratory made granular-granular impact craters have been used as model analogues of planetary impact craters. These kind of craters have been observed and studied using profilometry techniques that allow to retrieve important morphologic features from the impacted surface. In this work, we propose to use a Time-of-Flight camera (Microsoft Kinect One) for the acquisition of depth data. We show comparisons between the typically used technique and the analysis derived from the Time-of-Flight data. We also release craterslab, a Python library developed to automate most of the tasks from the process of studying impact craters produced by granular projectiles hitting on the surface of granular targets. The library is able to acquire, identify, and measure morphological features of impacted surfaces through the reconstruction of 3D topographic maps. Our results show that using a Time-of-Flight camera and automating the data processing with a software library for the systematic study of impact craters can produce very accurate results while reducing the time spent on different stages of the process.