Deep-sea image processing

High-resolution seafloor mapping often requires optical methods of sensing, to confirm interpretations made from sonar data. Optical digital imagery of seafloor sites can now provide very high resolution and also provides additional cues, such as color information for sediments, biota and divers rock types. During the cruise AT11-7 of the Woods Hole Oceanographic Institution (WHOI) vessel R/V Atlantis (February 2004, East Pacific Rise) visual imagery was acquired from three sources: (1) a digital still down-looking camera mounted on the submersible Alvin, (2) observer-operated 1-and 3-chip video cameras with tilt and pan capabilities mounted on the front of Alvin, and (3) a digital still camera on the WHOI TowCam (Fornari, 2003). Imagery from the first source collected on a previous cruise (AT7-13) to the Galapagos Rift at 86°W was successfully processed and mosaicked post-cruise, resulting in a single image covering area of about 2000 sq.m, with the resolution of 3 mm per pixel (Rzhanov et al., 2003). This paper addresses the issues of the optimal acquisition of visual imagery in deep-seaconditions, and requirements for on-board processing. Shipboard processing of digital imagery allows for reviewing collected imagery immediately after the dive, evaluating its importance and optimizing acquisition parameters, and augmenting acquisition of data over specific sites on subsequent dives.Images from the deepsea power and light (DSPL) digital camera offer the best resolution (3.3 Mega pixels) and are taken at an interval of 10 seconds (determined by the strobe\u27s recharge rate). This makes images suitable for mosaicking only when Alvin moves slowly (≪1/4 kt), which is not always possible for time-critical missions. Video cameras provided a source of imagery more suitable for mosaicking, despite its inferiority in resolution. We discuss required pre-processing and imageenhancement techniques and their influence on the interpretation of mosaic content. An algorithm for determination of camera tilt parameters from acquired imagery is proposed and robustness conditions are discussed

Modeling and interpolation of the ambient magnetic field by Gaussian processes

Anomalies in the ambient magnetic field can be used as features in indoor positioning and navigation. By using Maxwell's equations, we derive and present a Bayesian non-parametric probabilistic modeling approach for interpolation and extrapolation of the magnetic field. We model the magnetic field components jointly by imposing a Gaussian process (GP) prior on the latent scalar potential of the magnetic field. By rewriting the GP model in terms of a Hilbert space representation, we circumvent the computational pitfalls associated with GP modeling and provide a computationally efficient and physically justified modeling tool for the ambient magnetic field. The model allows for sequential updating of the estimate and time-dependent changes in the magnetic field. The model is shown to work well in practice in different applications: we demonstrate mapping of the magnetic field both with an inexpensive Raspberry Pi powered robot and on foot using a standard smartphone.Comment: 17 pages, 12 figures, to appear in IEEE Transactions on Robotic

Effortless Deep Training for Traffic Sign Detection Using Templates and Arbitrary Natural Images

Deep learning has been successfully applied to several problems related to autonomous driving. Often, these solutions rely on large networks that require databases of real image samples of the problem (i.e., real world) for proper training. The acquisition of such real-world data sets is not always possible in the autonomous driving context, and sometimes their annotation is not feasible (e.g., takes too long or is too expensive). Moreover, in many tasks, there is an intrinsic data imbalance that most learning-based methods struggle to cope with. It turns out that traffic sign detection is a problem in which these three issues are seen altogether. In this work, we propose a novel database generation method that requires only (i) arbitrary natural images, i.e., requires no real image from the domain of interest, and (ii) templates of the traffic signs, i.e., templates synthetically created to illustrate the appearance of the category of a traffic sign. The effortlessly generated training database is shown to be effective for the training of a deep detector (such as Faster R-CNN) on German traffic signs, achieving 95.66% of mAP on average. In addition, the proposed method is able to detect traffic signs with an average precision, recall and F1-score of about 94%, 91% and 93%, respectively. The experiments surprisingly show that detectors can be trained with simple data generation methods and without problem domain data for the background, which is in the opposite direction of the common sense for deep learning

Evaluating indoor positioning systems in a shopping mall : the lessons learned from the IPIN 2018 competition

The Indoor Positioning and Indoor Navigation (IPIN) conference holds an annual competition in which indoor localization systems from different research groups worldwide are evaluated empirically. The objective of this competition is to establish a systematic evaluation methodology with rigorous metrics both for real-time (on-site) and post-processing (off-site) situations, in a realistic environment unfamiliar to the prototype developers. For the IPIN 2018 conference, this competition was held on September 22nd, 2018, in Atlantis, a large shopping mall in Nantes (France). Four competition tracks (two on-site and two off-site) were designed. They consisted of several 1 km routes traversing several floors of the mall. Along these paths, 180 points were topographically surveyed with a 10 cm accuracy, to serve as ground truth landmarks, combining theodolite measurements, differential global navigation satellite system (GNSS) and 3D scanner systems. 34 teams effectively competed. The accuracy score corresponds to the third quartile (75th percentile) of an error metric that combines the horizontal positioning error and the floor detection. The best results for the on-site tracks showed an accuracy score of 11.70 m (Track 1) and 5.50 m (Track 2), while the best results for the off-site tracks showed an accuracy score of 0.90 m (Track 3) and 1.30 m (Track 4). These results showed that it is possible to obtain high accuracy indoor positioning solutions in large, realistic environments using wearable light-weight sensors without deploying any beacon. This paper describes the organization work of the tracks, analyzes the methodology used to quantify the results, reviews the lessons learned from the competition and discusses its future