P10. Road Cracking Area Percentage Evaluation Using Airborne Hyperspectral Imagery

Background: Although the airborne platform is efficient and economic compared to the popular ground vehicles in road condition survey (RCS), studies on applying it in RCS are limited. A former study on airborne hyperspectral imagery (AHI) tried to tie a band ratio to pavement condition index. Its result proved the value in AHI, yet pointed out the difficulty in quantitative evaluation. Methods: This study further explored the application of AHI in RCS, and fully took advantage of the entire spectrum shape, rather than a ratio. Aiming at the cracking area percentage (CAP) on the asphalt paved arterial road system in the City of Surrey, BC, the studied AHI was used to build a road CAP spectral library (SLib). The SLib contains five road classes separately with 0~1%, 2~5%, 6~10%, 11~30%, and 31~100% CAP. Then the study selects arterial roads in ten locations covering ~20 sq km to classify using the SLib. Results: The selected spectra well depict the reflectance increase from newer roads with less CAP to older roads with more CAP. But, the accuracies of the classification are only ~20%. By combining the first three classes and last two classes, the classification accuracy grows approximately 10~60% depending on the test tiles. Discussion & conclusion: Two conclusions are made: (1) the great misclassification among neighbor classes, e.g. class 0~1%, 2~5%, and 6~10, as well as 11~30% and 31~100% are often misclassified with each other; and (2) the result tends to overestimate the CAP. Interdisciplinary reflection: The engineering survey and geographic analysis contributed in extracting CAP from AHI. Its result is instructional in urban modeling and planning

West Antarctica snow accumulation trend study (1979-2011) from Snow Radar and ice core profiles

Ice sheets are under threat from increasing air and ocean temperatures. For Antarctica, observed changes are most apparent near the margins; inland the effects of a warming atmosphere and changing circulation patterns are less clear. Snow accumulation to the ice sheet offsets ice losses near the margin, and characterizing ice sheet accumulation rate is necessary for understanding ice sheet mass balance and predicting future sea level rise. Ice penetrating radar systems enable the measurement of ice sheet properties beneath the surface, such as ice thickness and internal layering. This study concentrates on mapping the depth of internal layers, and linking the layers to a chronology that allows snow accumulation rates over particular time periods to be determined. The focus is on one particular ice penetrating radar system: Snow Radar from the Center for Remote Sensing of Ice Sheet (CReSIS). The Snow Radar is a 2-8 GHz ultra-wideband (UWB), frequency-modulated, continuous-wave (FMCW) radar, having a ~5cm vertical resolution. The chronology of Snow Radar detected layers is validated to be annual layers using nearby ice core data and the results of a regional climate model (RACMO2.1/ANT). The measurement error of a manual layer picking procedure, and proximity of ice core density profiles to the Snow Radar data have been examined. The results show that the average error variance in manual picking is as small as 3.0e-4 m, and that it is reasonable to use ice core density profiles in Snow Radar data processing. Using Snow Radar data, a snow accumulation rate time series has been determined along two flight lines over West Antarctica. The spatiotemporal distribution of snow accumulation has been analyzed and possible explanations for such distribution are discussed. No significant trend is found in snow accumulation during the 33-year study period (1979-2011). The snow accumulation spatial distribution has been related to topography and wind, showing that snow accumulation has a negative correlation with elevation and is generally lower on leeward slopes than on the windward slopes

Tensor-based Hyperspectral Image Processing Methodology and its Applications in Impervious Surface and Land Cover Mapping

The emergence of hyperspectral imaging provides a new perspective for Earth observation, in addition to previously available orthophoto and multispectral imagery. This thesis focused on both the new data and new methodology in the field of hyperspectral imaging. First, the application of the future hyperspectral satellite EnMAP in impervious surface area (ISA) mapping was studied. During the search for the appropriate ISA mapping procedure for the new data, the subpixel classification based on nonnegative matrix factorization (NMF) achieved the best success. The simulated EnMAP image shows great potential in urban ISA mapping with over 85% accuracy. Unfortunately, the NMF based on the linear algebra only considers the spectral information and neglects the spatial information in the original image. The recent wide interest of applying the multilinear algebra in computer vision sheds light on this problem and raised the idea of nonnegative tensor factorization (NTF). This thesis found that the NTF has more advantages over the NMF when work with medium- rather than the high-spatial-resolution hyperspectral image. Furthermore, this thesis proposed to equip the NTF-based subpixel classification methods with the variations adopted from the NMF. By adopting the variations from the NMF, the urban ISA mapping results from the NTF were improved by ~2%. Lastly, the problem known as the curse of dimensionality is an obstacle in hyperspectral image applications. The majority of current dimension reduction (DR) methods are restricted to using only the spectral information, when the spatial information is neglected. To overcome this defect, two spectral-spatial methods: patch-based and tensor-patch-based, were thoroughly studied and compared in this thesis. To date, the popularity of the two solutions remains in computer vision studies and their applications in hyperspectral DR are limited. The patch-based and tensor-patch-based variations greatly improved the quality of dimension-reduced hyperspectral images, which then improved the land cover mapping results from them. In addition, this thesis proposed to use an improved method to produce an important intermediate result in the patch-based and tensor-patch-based DR process, which further improved the land cover mapping results

FC-Planner: A Skeleton-guided Planning Framework for Fast Aerial Coverage of Complex 3D Scenes

3D coverage path planning for UAVs is a crucial problem in diverse practical applications. However, existing methods have shown unsatisfactory system simplicity, computation efficiency, and path quality in large and complex scenes. To address these challenges, we propose FC-Planner, a skeleton-guided planning framework that can achieve fast aerial coverage of complex 3D scenes without pre-processing. We decompose the scene into several simple subspaces by a skeleton-based space decomposition (SSD). Additionally, the skeleton guides us to effortlessly determine free space. We utilize the skeleton to efficiently generate a minimal set of specialized and informative viewpoints for complete coverage. Based on SSD, a hierarchical planner effectively divides the large planning problem into independent sub-problems, enabling parallel planning for each subspace. The carefully designed global and local planning strategies are then incorporated to guarantee both high quality and efficiency in path generation. We conduct extensive benchmark and real-world tests, where FC-Planner computes over 10 times faster compared to state-of-the-art methods with shorter path and more complete coverage. The source code will be open at https://github.com/HKUST-Aerial-Robotics/FC-Planner.Comment: Submitted to ICRA2024. 6 Pages, 6 Figures, 3 Tables. Code: https://github.com/HKUST-Aerial-Robotics/FC-Planner. Video: https://www.bilibili.com/video/BV1h84y1D7u5/?spm_id_from=333.999.0.0&vd_source=0af61c122e5e37c944053b57e313025

AutoTrans: A Complete Planning and Control Framework for Autonomous UAV Payload Transportation

The robotics community is increasingly interested in autonomous aerial transportation. Unmanned aerial vehicles with suspended payloads have advantages over other systems, including mechanical simplicity and agility, but pose great challenges in planning and control. To realize fully autonomous aerial transportation, this paper presents a systematic solution to address these difficulties. First, we present a real-time planning method that generates smooth trajectories considering the time-varying shape and non-linear dynamics of the system, ensuring whole-body safety and dynamic feasibility. Additionally, an adaptive NMPC with a hierarchical disturbance compensation strategy is designed to overcome unknown external perturbations and inaccurate model parameters. Extensive experiments show that our method is capable of generating high-quality trajectories online, even in highly constrained environments, and tracking aggressive flight trajectories accurately, even under significant uncertainty. We plan to release our code to benefit the community.Comment: Accepted by IEEE Robotics and Automation Letter

MacFormer: Map-Agent Coupled Transformer for Real-time and Robust Trajectory Prediction

Predicting the future behavior of agents is a fundamental task in autonomous vehicle domains. Accurate prediction relies on comprehending the surrounding map, which significantly regularizes agent behaviors. However, existing methods have limitations in exploiting the map and exhibit a strong dependence on historical trajectories, which yield unsatisfactory prediction performance and robustness. Additionally, their heavy network architectures impede real-time applications. To tackle these problems, we propose Map-Agent Coupled Transformer (MacFormer) for real-time and robust trajectory prediction. Our framework explicitly incorporates map constraints into the network via two carefully designed modules named coupled map and reference extractor. A novel multi-task optimization strategy (MTOS) is presented to enhance learning of topology and rule constraints. We also devise bilateral query scheme in context fusion for a more efficient and lightweight network. We evaluated our approach on Argoverse 1, Argoverse 2, and nuScenes real-world benchmarks, where it all achieved state-of-the-art performance with the lowest inference latency and smallest model size. Experiments also demonstrate that our framework is resilient to imperfect tracklet inputs. Furthermore, we show that by combining with our proposed strategies, classical models outperform their baselines, further validating the versatility of our framework.Comment: Accepted by IEEE Robotics and Automation Letters. 8 Pages, 9 Figures, 9 Tables. Video: https://www.youtube.com/watch?v=XY388iI6sP