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Light detection and ranging (LiDAR) instrumentation is becoming more diverse in our world of today. One challenge in scaled physical hydrodynamic models in a laboratory setting is obtaining high resolution water surface elevation data while maintaining accuracy requirements. Accurate water surface elevations are a primary parameter in hydraulic models as they are a means of controlling/monitoring the physical model’s boundary conditions, analyzing model experiment results, and informing model conclusions. This study focuses on laser scanners that have ranging accuracies of at least +/-10 millimeters (mm) or better for the purpose of attaining LiDAR water surface elevation measurements in scaled physical hydrodynamic models in the laboratory setting using different materials on the water surface. While the current available methods have acceptable accuracies, the resolution is extremely limited. The objective of this research to improve the spatial coverage of water surface elevation measurements by using LiDAR instrumentation while maintaining acceptable error ranges.

This paper presents Triangle-To-Cloud (T2C), a new approach for point cloud change detection. The method is compared to the established method Multiscale Model to Model Cloud Comparison (M3C2) on the accuracy to detect changes, from a point cloud to another. The comparison is performed on Light Detection and Ranging (LiDAR) mappings from an Ouster OS0-128 LiDAR sensor. Both T2C and M3C2 are tested with different parameters in all of the experiments conducted for evaluation. This work demonstrates in the experiments that T2C can outperform M3C2 in its ability to detect changes.

The aim of this project is to implement a Radio Controlled (RC) car with a Light Detection and Ranging (LiDAR) sensor and a stereoscopic camera setup based on the Robot Operating System (ROS) to conduct Simultaneous Localization and Mapping (SLAM). The LiDAR sensor used is a 2D LiDAR, RPlidar A1, and the stereoscopic camera setup is made of two monocular cameras, Raspberry Pi Camera v2. The sensors were mounted on the RC car and connected using two Raspberry Pi microcomputers.  The 2D LiDAR sensor was used for two-dimensional mapping and the stereo vision from the camera setup for three-dimensional mapping. RC car movement information, odometry, necessary for SLAM was derived using either the LiDAR data or the data from the stereoscopic camera setup. Two means of SLAM were implemented both separately and together for mapping an office space. The SLAM algorithms adopted the Real Time Appearance Based Mapping (RTAB-map) package in the open-source ROS.    The results of the mapping indicated that the RPlidar A1 was able to provide a precise mapping, but showed difficulty when mapping in large circular patterns as the odometry drift resulted in the mismatch of the current mapping with the earlier mapping of the same positions and secondly in localization when turning quickly. The camera setup derived more information about surrounding and showed more robust odometry. However, the setup performed poorly for the mapping of visual loop closures, i.e., the current mapping did not match the earlier mapping of earlier visited positions.

Monitoring carbon stock has emerged as a critical environmental problem among several worldwide organizations and collaborations in the context of global warming and climate change. This study seeks to provide a remote sensing solution based on three types of data, to explore the feasibility and reliability of estimating aboveground biomass (AGB) in order to improve the efficiency of monitoring carbon stock. The study attempted to investigate the potential of using Google Earth Engine (GEE), and the combinations of different datasets from Sentinel-1 (SAR), Sentinel-2 multispectral imagery, and LiDAR data to estimate AGB, by using the random forest algorithm (RF). Two models were proposed: the first one (Model 1) detected the AGB temporal changes from 2016 to 2021 in Southern Sweden; while the second one (Model 2) focused on Hultsfred municipality and studied the influence of different variables including the canopy height. Besides, six experimental groups of variables were tested to determine the performance of using different types of remote sensing data. We validated these two models with the observed AGB, and the findings showed that the combination of SAR polarization, multisprectral bands, vegetation indices able to estimate AGB for Model 1. In addition, Model 2 showed that further using the canopy height data can further improve the estimation.  We also found out that the spectral bands from Sentinel-2 contributed the most to AGB estimation for Model 1 in terms of: bands B3 (Green), B4 (Red), B5 (Red edge), B11 (SWIR), B12 (SWIR); and, vegetation indices of RVI, DVI, and EVI. On the other hand, for Model 2, B1(Ultra blue), B4 (Red), EVI, SAVI, and the canopy height are the most crucial variables for estimating AGB. Besides, the radar backscatter values using VV and VH modes from Sentienl-1 were both important for Models 1 and 2. For Model 1, the experimental group with the best accuracy was the group that used all variable combinations from Sentinel-1 and 2, and its   was 0.33~0.74. For Model 2, the group that used all the variables, in addition to the canopy height performed the best, where its   is 0.91. These therefore showed the benefit of integrating different remote sensing data sources.  In conclusion, this study showed the potential of using RF and GEE to estimate AGB in Southern Sweden. Furthermore, this study also shows the possibility of handling large dataset for a large scale area, at the resolution of 10 m, and producing time series AGB maps from 2016 to 2021. This can help enhance our understanding of AGB temporal changes and carbon stock detection in Southern Sweden, that can provide valuable insights for forest management and carbon monitoring.

A comparison of performance between tradition support vector machine (SVM), single kernel, multiple kernel learning (MKL), and modern deep learning (DL) classifiers are observed in this thesis. The goal is to implement different machine-learning classification system for object detection of three dimensional (3D) Light Detection and Ranging (LiDAR) data. The linear SVM, non linear single kernel, and MKL requires hand crafted features for training and testing their algorithm. The DL approach learns the features itself and trains the algorithm. At the end of these studies, an assessment of all the different classification methods are shown.

Dams are relatively simple hydraulic structures that provide vital services to communities in the United States (U.S.). However, many of the dams in the (U.S.) have surpassed their design life. Dams experience changes from external threats that result in deformation of the structure. Traditional surveying techniques provide limited information on deformation in pre-determined areas of a structure, but the collection effort can often be lengthy. In this research, different instruments used for change detection were reviewed and Terrestrial Laser Scanning (TLS), also known as ground-based Light Detection and Ranging (LiDAR), was selected as the most probable method to accurately evaluate deformation in dams. TLS is a remote sensing instrument that uses light to form a pulsed laser to measure ranges to variable targets, and it provides the ability to measure displacement with high accuracy using dense point clouds collected in a short amount of time. Deformation is identified by measuring changes in point clouds generated by TLS. The accuracy of TLS to identify deformation was tested on a thin arch dam at the Big Black Test site in Vicksburg, Mississippi, using the TLS system, Terrestrial Laser Scanner RIEGL VZ-400, for data collection and for registering scan positons between a pre-test condition and a post-test condition. Final data analysis was performed using Microstation TopoDOTTM Wall Monitoring Tool.

As technology advances with each new day, so do the applications and uses of the different modalities of technology, including transportation, particularly in ADAS vehicles. These systems allow the vehicle to avoid collisions, change lanes, adjust the vehicle’s speed, and more without the need of driver input. However, each sensor type has a weakness, and most advanced driver- assisted system (ADAS) vehicles rely heavily on sensors, such as RGB cameras, radars, and LiDAR sensors. These visual-based sensors may collect very noisy data in cloudy, raining, foggy, or other obscuring phenomena. Radar, on the other hand, does not rely on visual information to produce meaningful output, and instead collects range and velocity information. This research aims to use radar technology for human motion classification using traffic signaling based on motions generally used in the American traffic system, while also fusing data from other visual sensors and validating results using neural networks.

Off-road autonomy is a challenging topic that requires robust systems to both understand and navigate complex environments. While on-road autonomy has seen a major expansion in recent years in the consumer space, off-road systems are mostly relegated to niche applications. However, these applications can provide safety and navigation to dangerous areas that are the most suited for autonomy tasks. Traversability analysis is at the core of many of the algorithms employed in these topics. In this thesis, a Clearpath Robotics Jackal vehicle is equipped with a 3D Ouster laser scanner to define and traverse off-road environments. The Mississippi State University Autonomous Vehicle Simulator (MAVS) and the Navigating All Terrains Using Robotic Exploration (NATURE) autonomy stack are used in conjunction with the small-scale vehicle platform to traverse uneven terrain and collect data. Additionally, the NATURE stack is used as a point of comparison between a MAVS simulated and physical Clearpath Robotics Jackal vehicle in testing.