Overview of the North Ecliptic Pole multi-wavelength survey (NEP-DEEP)

An overview of the North Ecliptic Pole (NEP) deep multi-wavelength survey covering from X-ray to radio wavelengths is presented. The main science objective of this multi-wavelength project is to unveil the star-formation and AGN activities obscured by dust in the violent epoch of the Universe (z=0.5-2), when star-formation and black hole activities were much stronger than at present. The NEP deep survey with AKARI/IRC consists of two survey projects: shallow wide (8.2 sq. deg, NEP-Wide) and the deep one (0.6 sq. deg, NEP-Deep). The NEP-Deep provides us with a 15 μm or 18 μm selected sample of several thousands of galaxies, the largest sample ever made at these wavelengths. A continuous filter coverage at mid-IR wavelengths (7, 9, 11, 15, 18, and 24 μm) is unique and vital to diagnose the contribution from starbursts and AGNs in the galaxies at the violent epoch. The recent updates of the ancillary data are also provided: optical/near-IR magnitudes (Subaru, CFHT), X-ray (Chandra), FUV/NUV (GALEX), radio (WSRT, GMRT), optical spectra (Keck/DEIMOS etc.), Subaru/FMOS, Herschel/SPIRE, and JCMT/SCUBA-2

What localizes beneath:A metric multisensor localization and mapping system for autonomous underground mining vehicles

Robustly and accurately localizing vehicles in underground mines is particularly challenging due to the unavailability of GPS, variable and often poor lighting conditions, visual aliasing in long tunnels, and airborne dust and water. In this paper, we present a novel, infrastructure‐less, multisensor localization method for robust autonomous operation within underground mines. The proposed method integrates with existing mine site commissioning and operation procedures and includes both an offline map‐building process and an online localization algorithm. The approach combines the strengths of visual‐based place recognition, LIDAR‐based localization, and odometry in a particle filter fusion process. We provide an extensive experimental validation using new large data sets acquired in two operational Australian underground hard‐rock mines (including a 600m‐deep multilevel mine with approximately 33km of mapping data and 7km of vehicle localization) by actual mining vehicles during production operations. We demonstrate a significant increase in localization accuracy over prior state‐of‐the‐art SLAM research systems and real‐time operation, with processing times in the order of 10 Hz. We present results showing a mean error of 0.68 m from the Queensland Mine data set and 1.32 m from the New South Wales Mine data set and at least 86% reduction in error compared with prior state of the art. We also analyze the impact of the particle filter parameters with respect to localization accuracy. Together this study represents a new approach to positioning systems for currently deployed autonomous vehicles within underground mine environments