<p>A Velodyne Puck VLP-16 LiDAR and a Carnegie Robotics Multisense S21 stereo camera were placed in an environmental testing chamber to investigate dust and lighting effects on depth returns. The environmental testing chamber was designed and built with varied lighting conditions with corn dust plumes forming the atmosphere. Specific software employing ROS, Python, and OpenCV were written for point cloud streaming and publishing. Dust chamber results showed while dust effects were present in point clouds produced by both instruments, the stereo camera was able to “see” the far wall of the chamber and did not image the dust plume, unlike the LiDAR sensor. The stereo camera was also set up to measure the volume of total mixed ration (TMR) and shelled grain in various volume scenarios with mixed surface terrains. Calculations for finding actual pixel area based on depth were utilized along with a volume formula exploiting the depth capability of the stereo camera for the results. Resulting accuracy was good for a target of 8 liters of shelled corn with final values between 6.8 and 8.3 liters from three varied surface scenarios. Lessons learned from the chamber and volume measurements were applied to loading large grain vessels being filled from a 750-bushel grain cart in the form of calculating the volume of corn grain and tracking the location of the vessel in near real time. Segmentation, masking, and template matching were the primary software tools used within ROS, OpenCV, and Python. The S21 was the center hardware piece. Resulting video and images show some lag between depth and color images, dust blocking depth pixels, and template matching misses. However, results were sufficient to show proof of concept of tracking and volume estimation. </p>

The study of this research has a challenge of learning data gathering sensor programming and design of electronic sensor circuit. The cost of autonomous vehicle development is expensive compared to purchasing an economy vehicle such as the Hyundai Elantra. Keeping the development cost down is critical to maintaining a competitive edge on vehicle pricing with newer technologies. Autonomous vehicle sensor integration was designed and then tested for the driving vision data-gathering system that requires the system to gather driving vision data utilizing area scan sensors, Lidar, ultrasonic sensor, and camera on real road scenarios. The project utilized sensors such as cheap cost LIDAR, which is that drone is used for on the road testing; other sensors include myRIO (myRIO Hardware), LabVIEW (LabVIEW software), LIDAR-Lite v3 (Garmin, 2019), Ultrasonic sensor, and Wantai stepper motor (Polifka, 2020). This research helps to reduce the price of usage of autonomous vehicle driving systems in the city. Due to resolution and Lidar detecting distance, the test environment is limited to within city areas. Lidar is the most expensive equipment on autonomous vehicle driving data gathering systems. This study focuses on replacing expensive Lidar, ultrasonic sensor, and camera to drone scale low-cost Lidar to real size vehicle. With this study, economic expense autonomous vehicle driving data acquisition is possible. Lowering the price of autonomous vehicle driving data acquisition increases involving new companies on the autonomous vehicle market. Multiple testing with multiple cars is possible. Since multiple testing at the same time is possible, collecting time reduces.

<div><p>Analyzing behaviors of objects on the road is a complex task that requires data from various sensors and their fusion to recreate movement of objects with a high degree of accuracy. A data collection and processing system are thus needed to track the objects accurately in order to make an accurate and clear map of the trajectories of objects relative to various coordinate frame(s) of interest in the map. Detection and tracking moving objects (DATMO) and Simultaneous localization and mapping (SLAM) are the tasks that needs to be achieved in conjunction to create a clear map of the road comprising of the moving and static objects.</p> <p> These computational problems are commonly solved and used to aid scenario reconstruction for the objects of interest. The tracking of objects can be done in various ways, utilizing sensors such as monocular or stereo cameras, Light Detection and Ranging (LIDAR) sensors as well as Inertial Navigation systems (INS) systems. One relatively common method for solving DATMO and SLAM involves utilizing a 3D LIDAR with multiple monocular cameras in conjunction with an inertial measurement unit (IMU) allows for redundancies to maintain object classification and tracking with the help of sensor fusion in cases when sensor specific traditional algorithms prove to be ineffectual when either sensor falls short due to their limitations. The usage of the IMU and sensor fusion methods relatively eliminates the need for having an expensive INS rig. Fusion of these sensors allows for more effectual tracking to utilize the maximum potential of each sensor while allowing for methods to increase perceptional accuracy. </p> <p>The focus of this thesis will be the dock-less e-scooter and the primary goal will be to track its movements effectively and accurately with respect to cars on the road and the world. Since it is relatively more common to observe a car on the road than e-scooters, we propose a data collection system that can be built on top of an e-scooter and an offline processing pipeline that can be used to collect data in order to understand the behaviors of the e-scooters themselves. In this thesis, we plan to explore a data collection system involving a 3D LIDAR sensor and multiple monocular cameras and an IMU on an e-scooter as well as an offline method for processing the data to generate data to aid scenario reconstruction. </p><br></div>

<div>The accelerating expansion of agricultural and urban areas fragments and degrades forests</div><div>and their capacity to provide essential ecosystem services while increasing physiological stress</div><div>and mortality rates of trees growing near forest edges. Previous studies have documented that</div><div>edges are hotter and drier than forest interiors and trees nearer the edge grow slower. However,</div><div>the physical structure of a forest’s canopy may serve to mitigate to these effects. This study</div><div>quantifies forest fragmentation across the Central Hardwoods Region (CHR; containing Missouri,</div><div>Illinois, and Indiana) and characterizes structural differences between the canopies of forest edges</div><div>and forest interiors. Importantly, we distinguish between edges that neighbor developed land and</div><div>agricultural lands since these landcover types may impose distinct effects on forest structure. We</div><div>characterized forest canopy structure in a subset of the CHR region using the 2016-2020 Indiana</div><div>3DEP Lidar Program data. Our findings indicate edge forest (forests within 30m of an edge) makes</div><div>up 29.8% of the total forest in our study extent, with urban and agricultural edges accounting for</div><div>17.8% and 72.8% of the edge edges in the region, respectively. Analysis of 15 separate structural</div><div>metrics derived from aerial laser scanning (ALS) showed no significant structural differences</div><div>between developed and agricultural edge canopies but did find differences between structure of</div><div>canopies in forest cores and those in forest edges of any kind. As developed and agricultural lands</div><div>increase so too will forest fragmentation and the creation of new forest edges. If there are no</div><div>significant differences between forest edge types, then we could begin to treat edges without</div><div>distinction. This could lead to simplified management practices for foresters and urban foresters</div><div>alike to protect and preserve forest fragments.</div>

<p>Emerging mobile mapping systems include a wide range of platforms, for instance, manned aircraft, unmanned aerial vehicles (UAV), terrestrial systems like trucks, tractors, robots, and backpacks, that can carry multiple sensors including LiDAR scanners, cameras, and georeferencing units. Such systems can maneuver in the field to quickly collect high-resolution data, capturing detailed information over an area of interest. With the increased volume and distinct characteristics of the data collected, practical quality control procedures that assess the agreement within/among datasets acquired by various sensors/systems at different times are crucial for accurate, robust interpretation. Moreover, the ability to derive semantic information from acquired data is the key to leveraging the complementary information captured by mobile mapping systems for diverse applications. This dissertation addresses these challenges for different systems (airborne and terrestrial), environments (urban and rural), and applications (agriculture, archaeology, hydraulics/hydrology, and transportation).</p> <p>In this dissertation, quality control procedures that utilize features automatically identified and extracted from acquired data are developed to evaluate the relative accuracy between multiple datasets. The proposed procedures do not rely on manually deployed ground control points or targets and can handle challenging environments such as coastal areas or agricultural fields. Moreover, considering the varying characteristics of acquired data, this dissertation improves several data processing/analysis techniques essential for meeting the needs of various applications. An existing ground filtering algorithm is modified to deal with variation in point density; digital surface model (DSM) smoothing and seamline control techniques are proposed for improving the orthophoto quality in agricultural fields. Finally, this dissertation derives semantic information for diverse applications, including 1) shoreline retreat quantification, 2) automated row/alley detection for plant phenotyping, 3) enhancement of orthophoto quality for tassel/panicle detection, and 4) point cloud semantic segmentation for mapping transportation corridors. The proposed approaches are tested using multiple datasets from UAV and wheel-based mobile mapping systems. Experimental results verify that the proposed approaches can effectively assess the data quality and provide reliable interpretation. This dissertation highlights the potential of modern mobile mapping systems to map challenging environments for a variety of applications.</p>