Adversarial Training for Adverse Conditions: Robust Metric Localisation using Appearance Transfer

We present a method of improving visual place recognition and metric localisation under very strong appear- ance change. We learn an invertable generator that can trans- form the conditions of images, e.g. from day to night, summer to winter etc. This image transforming filter is explicitly designed to aid and abet feature-matching using a new loss based on SURF detector and dense descriptor maps. A network is trained to output synthetic images optimised for feature matching given only an input RGB image, and these generated images are used to localize the robot against a previously built map using traditional sparse matching approaches. We benchmark our results using multiple traversals of the Oxford RobotCar Dataset over a year-long period, using one traversal as a map and the other to localise. We show that this method significantly improves place recognition and localisation under changing and adverse conditions, while reducing the number of mapping runs needed to successfully achieve reliable localisation.Comment: Accepted at ICRA201

The Right (Angled) Perspective: Improving the Understanding of Road Scenes Using Boosted Inverse Perspective Mapping

Many tasks performed by autonomous vehicles such as road marking detection, object tracking, and path planning are simpler in bird's-eye view. Hence, Inverse Perspective Mapping (IPM) is often applied to remove the perspective effect from a vehicle's front-facing camera and to remap its images into a 2D domain, resulting in a top-down view. Unfortunately, however, this leads to unnatural blurring and stretching of objects at further distance, due to the resolution of the camera, limiting applicability. In this paper, we present an adversarial learning approach for generating a significantly improved IPM from a single camera image in real time. The generated bird's-eye-view images contain sharper features (e.g. road markings) and a more homogeneous illumination, while (dynamic) objects are automatically removed from the scene, thus revealing the underlying road layout in an improved fashion. We demonstrate our framework using real-world data from the Oxford RobotCar Dataset and show that scene understanding tasks directly benefit from our boosted IPM approach.Comment: equal contribution of first two authors, 8 full pages, 6 figures, accepted at IV 201

Generating All the Roads to Rome: Road Layout Randomization for Improved Road Marking Segmentation

Road markings provide guidance to traffic participants and enforce safe driving behaviour, understanding their semantic meaning is therefore paramount in (automated) driving. However, producing the vast quantities of road marking labels required for training state-of-the-art deep networks is costly, time-consuming, and simply infeasible for every domain and condition. In addition, training data retrieved from virtual worlds often lack the richness and complexity of the real world and consequently cannot be used directly. In this paper, we provide an alternative approach in which new road marking training pairs are automatically generated. To this end, we apply principles of domain randomization to the road layout and synthesize new images from altered semantic labels. We demonstrate that training on these synthetic pairs improves mIoU of the segmentation of rare road marking classes during real-world deployment in complex urban environments by more than 12 percentage points, while performance for other classes is retained. This framework can easily be scaled to all domains and conditions to generate large-scale road marking datasets, while avoiding manual labelling effort.Comment: presented at ITSC 201

Limnology and plankton diversity of salt lakes from Transylvanian Basin (Romania): A review

In the present work, we review the current knowledge on genesis, limnology and biodiversity of salt lakes distributed around the inner contour of Eastern Carpathian arc (Transylvanian Basin, Central Romania). Transylvanian salt lakes formed on ancient halite (NaCl) deposits following natural processes or quarrying activities.  Most of these lakes are located in eastern (Sovata area), southern (Ocna Sibiului), and western (Turda-Cojocna) parts of the Transylvanian Basin, have small surfaces (0.1-4 ha), variable depths (2-100 m), are hypersaline (>10%, w/v, total salts, mainly NaCl) and permanently stratified. As consequence of steady salinity/density gradient, heat entrapment below surface layer (i.e., heliothermy) develops in several Transylvanian lakes. The physical and chemical water stratification is mirrored in the partition of plankton diversity. Lakes with less saline (2-10% salinity) water layers appear to harbor halotolerant representatives of phyto- (e.g., marine native Picochlorum spp. and Synechococcus spp.), zoo- (e.g., Moina salina), and bacterioplankton (e.g., Actinobacteria, Verrucomicobia), whereas halophilic plankton communities (e.g., green algae Dunaliella sp., brine shrimp Artemia sp., and members of Halobacteria class) dominate in the oxic surface of hypersaline (>10% salinity) lakes. Molecular approaches (e.g., PCR-DGGE, 16S rRNA gene-based clone libraries, and DNA metabarcoding) showed that the O2-depleted bottom brines of deep meromictic Transylvanian lakes are inhabited by known extremely halophilic anaerobes (e.g. sulfate-reducing Delta-Proteobacteria, fermenting Clostridia, methanogenic and polymer-degrading archaea) in addition to representatives of uncultured/unclassified prokaryotic lineages. Overall, the plankton communities thriving in saline Transylvanian lakes seem to drive full biogeochemical cycling of main elements. However, the trophic interactions (i.e., food web structure and energy flow) as well as impact of human activities and predicted climate changes are yet to be assessed in these unique ecosystems with little or no match to analogous salt lakes worldwide