Robust Road Modeling based on a Hierarchical Bipartite Graph

Driver assistance systems based on video processing deliver a number of warnings to the driver, such as lane departure, lane invasion by other vehicles, collision prediction, etc. This have been a field of intense research for many years, providing solutions based on road models where vehicles are afterwards detected and tracked. Robustness is essential in this field of road safety where outliers represent one of the major problems for road modeling. The motivation of this work is to provide a robust and, at the same time, flexible road model which identifies a variable number of lanes, their widths, the curvature of the road and the position of the vehicle in its lane. The major advantage of this model is that the system gives confidence measures for each lane, determining which lanes are actually present and which not. The model is structured as a hierarchical bipartite graph which simplifies information management, reduces sub-module dependencies and classifies elements of the road in different levels. At each level different strategies are applied, following four overall steps: measurement, estimation, evaluation and extrapolation, which lead to enhanced road model accuracy, reliability and flexibility. Several experimental results are provided, showing the robustness of the system, its stability and accurate results for large test paths

A Comprehensive Survey of Deep Learning in Remote Sensing: Theories, Tools and Challenges for the Community

In recent years, deep learning (DL), a re-branding of neural networks (NNs), has risen to the top in numerous areas, namely computer vision (CV), speech recognition, natural language processing, etc. Whereas remote sensing (RS) possesses a number of unique challenges, primarily related to sensors and applications, inevitably RS draws from many of the same theories as CV; e.g., statistics, fusion, and machine learning, to name a few. This means that the RS community should be aware of, if not at the leading edge of, of advancements like DL. Herein, we provide the most comprehensive survey of state-of-the-art RS DL research. We also review recent new developments in the DL field that can be used in DL for RS. Namely, we focus on theories, tools and challenges for the RS community. Specifically, we focus on unsolved challenges and opportunities as it relates to (i) inadequate data sets, (ii) human-understandable solutions for modelling physical phenomena, (iii) Big Data, (iv) non-traditional heterogeneous data sources, (v) DL architectures and learning algorithms for spectral, spatial and temporal data, (vi) transfer learning, (vii) an improved theoretical understanding of DL systems, (viii) high barriers to entry, and (ix) training and optimizing the DL.Comment: 64 pages, 411 references. To appear in Journal of Applied Remote Sensin

Learning to See the Wood for the Trees: Deep Laser Localization in Urban and Natural Environments on a CPU

Localization in challenging, natural environments such as forests or woodlands is an important capability for many applications from guiding a robot navigating along a forest trail to monitoring vegetation growth with handheld sensors. In this work we explore laser-based localization in both urban and natural environments, which is suitable for online applications. We propose a deep learning approach capable of learning meaningful descriptors directly from 3D point clouds by comparing triplets (anchor, positive and negative examples). The approach learns a feature space representation for a set of segmented point clouds that are matched between a current and previous observations. Our learning method is tailored towards loop closure detection resulting in a small model which can be deployed using only a CPU. The proposed learning method would allow the full pipeline to run on robots with limited computational payload such as drones, quadrupeds or UGVs.Comment: Accepted for publication at RA-L/ICRA 2019. More info: https://ori.ox.ac.uk/esm-localizatio