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The main study objective was to develop robust processing and analysis techniques to facilitate the use of small-footprint lidar data for estimating forest biophysical parameters measuring individual trees identifiable on the three-dimensional lidar surface. This study derived the digital terrain model from lidar data using an iterative slope-based algorithm and developed processing methods for directly measuring tree height, crown diameter, and stand density. The lidar system used for this study recorded up to four returns per pulse, with an average footprint of 0.65 m and an average distance between laser shots of 0.7 m. The lidar data set was acquired over deciduous, coniferous, and mixed stands of varying age classes and settings typical of the southeastern United States (37° 25' N, 78° 41' W). Lidar processing techniques for identifying and measuring individual trees included data fusion with multispectral optical data and local filtering with both square and circular windows of variable size. The window size was based on canopy height and forest type. The crown diameter was calculated as the average of two values measured along two perpendicular directions from the location of each tree top, by fitting a four-degree polynomial on both profiles. The ground-truth plot design followed the U.S. National Forest Inventory and Analysis (FIA) field data layout. The lidar-derived tree measurements were used with regression models and cross-validation to estimate plot level field inventory data, including volume, basal area, and biomass. FIA subplots of 0.017 ha each were pooled together in two categories, deciduous trees and pines. For the pine plots, lidar measurements explained 97% of the variance associated with the mean height of dominant trees. For deciduous plots, regression models explained 79% of the mean height variance for dominant trees. Results for estimating crown diameter were similar for both pines and deciduous trees, with R2 values of 0.62-0.63 for the dominant trees. R2 values for estimating biomass were 0.82 for pines (RMSE 29 Mg/ha) and 0.32 for deciduous (RMSE 44 Mg/ha). Overall, plot level tree height and crown diameter calculated from individual tree lidar measurements were particularly important in contributing to model fit and prediction of forest volume and biomass. / Ph. D.

Safety risk evaluation is critical in autonomous vehicle applications. This research aims to develop, implement, and validate new safety monitoring methods for navigation in Global Navigation Satellite System (GNSS)-denied environments. The methods quantify uncertainty in sensors and algorithms that exploit the complementary properties of light detection and ranging (LiDAR) and inertial measuring units (IMU). This dissertation describes the following four contributions. First, we focus on sensor augmentation for landmark-based localization. We develop new IMU/LiDAR integration methods that guarantee a bound on the integrity risk, which is the probability that the navigation error exceeds predefined acceptability limits. IMU data improves LiDAR position and orientation (pose) prediction and LiDAR limits the IMU error drift over time. In addition, LiDAR return-light intensity measurements improve landmarks recognition. As compared to using the sensors individually, tightly-coupled IMU/LiDAR not only increases pose estimation accuracy but also reduces the risk of incorrectly associating perceived features with mapped landmarks. Second, we consider algorithm improvements. We derive and analyze a new data association method that provides a tight bound on the risk of incorrect association for LiDAR feature-based localization. The new data association criterion uses projections of the extended Kalman filter's (EKF) innovation vector rather than more conventional innovation vector norms. This method decreases the integrity risk by improving our ability to predict the risk of incorrect association. Third, we depart from landmark-based approaches. We develop a spherical grid-based localization method that leverages quantization theory to bound navigation uncertainty. This method is integrated with an iterative EKF to establish an analytical bound on the vehicle's pose estimation error. Unlike landmark-based localization which requires feature extraction and data association, this method uses the entire LiDAR point cloud and is robust to extraction and association failures. Fourth, to validate these methods, we designed and built two testbeds for indoor and outdoor experiments. The indoor testbed includes a sensor platform installed on a rover moving on a figure-eight track in a controlled lab environment. The repeated figure-eight trajectory provides empirical pose estimation error distributions that can directly be compared with analytical error bounds. The outdoor testbed required another set of navigation sensors for reference truth trajectory generation. Sensors were mounted on a car to validate our algorithms in a realistic automotive driving environment. / Doctor of Philosophy / Advances in computing and sensing technologies have enabled large scale demonstrations of autonomous vehicle operations including pilot programs for self-driving cars on public roads. However, a key question that has yet to be answered is about how safe these vehicles really are. "Autonomously" driving millions of miles (with a trained safety driver taking over control to prevent potential collisions) is insufficient to prove fatality rates matching human performance, i.e., lower than 1 per 100,000,000 miles driven. The safety of an autonomous vehicle depends on the safety of its individual subsystems, components, connected infrastructure, etc. In this research, we evaluate the safety of the navigation subsystem which uses sensor information to determine the vehicle's location and orientation. We focus on light detection and ranging (LiDAR)and inertial measuring units (IMU). A LiDAR provides a point cloud representation of the environment by measuring distances to surrounding objects using beams of infrared light (laser beams) sent at regular angular intervals. An IMU measures the acceleration and angular velocity of the vehicle. We assume that a map of the environment is available. In the first part of this research, we extract recognizable objects from the LiDAR point cloud and match them with those in the map: this process helps estimate the vehicle's position and orientation. We identify the process' limitations that include incorrectly matching sensed and mapped landmarks. We develop new methods to quantify their impacts on localization errors, which we then reduce by incorporating additional IMU data. In the second part of this dissertation, we design and evaluate a new approach specifically aimed at provably increasing confidence in landmark matching, thereby improving vehicle navigation safety. Third, instead of isolating individual landmarks, we use the LiDAR point cloud as a whole and match it directly with the map. The challenge with this approach was in efficiently and accurately quantifying the confidence that can be placed in the vehicle's navigation solution. We tested these navigation methods using experimental data collected in a controlled lab environment and in a real-world scenario.

Scaled test-beds (STBs) are popular tools to develop and physically test algorithms for advanced driving systems, but often lack automotive-grade radars in their sensor suites. To overcome resolution issues when using a radar at small scale, a high-level sensor fusion approach between the radar and automotive-grade LiDAR was proposed. The sensor fusion approach was expected to leverage the higher spatial resolution of the LiDAR effectively. First, multi object radar tracking software (RTS) was developed to track a maneuvering full-scale vehicle using an extended Kalman filter (EKF) and the joint probabilistic data association (JPDA). Second, a 1/5th scaled vehicle performed the same vehicle maneuvers but scaled to approximately 1/5th the distance and speed. When taking the scaling factor into consideration, the RTS' positional error at small scale was, on average, over 5 times higher than in the full-scale trials. Third, LiDAR object sensor tracks were generated for the small-scale trials using a Velodyne PUCK LiDAR, a simplified point cloud clustering algorithm, and a second EKF implementation. Lastly, the radar sensor tracks and LiDAR sensor tracks served as inputs to a high-level track-to-track fuser for the small-scale trials. The fusion software used a third EKF implementation to track fused objects between both sensors and demonstrated a 30% increase in positional accuracy for a majority of the small-scale trials when compared to using just the radar or just the LiDAR to track the vehicle. The proposed track fuser could be used to increase the accuracy of RTS algorithms when operating in small scale and allow STBs to better incorporate automotive radars into their sensor suites. / Master of Science / Research and development platforms, often supported by robust prototypes, are essential for the development, testing, and validation of automated driving functions. Thousands of hours of safety and performance benchmarks must be met before any advanced driver assistance system (ADAS) is considered production-ready. However, full-scale testbeds are expensive to build, labor-intensive to design, and present inherent safety risks while testing. Scaled prototypes, developed to model system design and vehicle behavior in targeted driving scenarios, can minimize these risks and expenses. Scaled testbeds, more specifically, can improve the ease of safety testing future ADAS systems and help visualize test results and system limitations, better than software simulations, to audiences with varying technical backgrounds. However, these testbeds are not without limitation. Although small-scale vehicles may accommodate similar on-board systems to its full-scale counterparts, as the vehicle scales down the resolution from perception sensors decreases, especially from on board radars. With many automated driving functions relying on radar object detection, the scaled vehicle must host radar sensors that function appropriately at scale to support accurate vehicle and system behavior. However, traditional radar technology is known to have limitations when operating in small-scale environments. Sensor fusion, which is the process of merging data from multiple sensors, may offer a potential solution to this issue. Consequently, a sensor fusion approach is presented that augments the angular resolution of radar data in a scaled environment with a commercially available Light Detection and Ranging (LiDAR) system. With this approach, object tracking software designed to operate in full-scaled vehicles with radars can operate more accurately when used in a scaled environment. Using this improvement, small-scale system tests could confidently and quickly be used to identify safety concerns in ADAS functions, leading to a faster and safer product development cycle.