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Abstract-In this paper, we present a novel computer vision framework for precise localization of a mobile robot on sidewalks. In our framework, we combine stereo camera images, visual odometry, satellite map matching, and a sidewalk probability transfer function obtained from street maps in order to attain globally corrected localization results. The framework is capable of precisely localizing a mobile robot platform that navigates on sidewalks, without the use of traditional wheel odometry, GPS or INS inputs. On a complex 570-meter sidewalk route, we show that we obtain superior localization results compared to visual odometry and GPS.

20We examined iridium (Ir) anomalies at the Cretaceous/Paleogene (K/Pg) 21 boundary in siliciclastic shallow marine cores of the New Jersey Coastal Plain, USA, that 22 were deposited at an intermediate distance (~2500 km) from the Chicxulub, Mexico 23 crater. Although closely spaced and generally biostratigraphically complete, the cores 24show heterogeneity in terms of preservation of the ejecta layers, maximum concentration 25 of Ir measured (~0.1 ppb -2.4 ppb), and total thickness of the Ir-enriched interval (11 -26 119 cm). We analyzed the shape of the Ir profiles with a Lagrangian particle-tracking 27 model of sediment mixing. Fits between the mixing model and measured Ir profiles, as 28 well as visible burrows in the cores, show that the shape of the Ir profiles was determined 29 primarily by sediment mixing via bioturbation. In contrast, Tighe Park 1 and Bass River 30 cores show post-depositional remobilization of Ir by geochemical processes. There is a 31 strong inverse relationship between the maximum concentration of Ir measured and the 32 thickness of the sediments over which Ir is spread. We show that the depth-integrated Ir 33 inventory is similar in the majority of the cores, indicating that the total Ir delivery at 34 time of the K/Pg event was spatially homogenous over this region. Though delivered 35 through a near-instantaneous source, stratospheric dispersal, and settling, our study shows 36 that non-uniform Ir profiles develop due to changes in the regional delivery and post-37 depositional modification by bioturbation and geochemical processes. 38

In this paper we present a vision-based method for instant global localization from a given aerial image. The approach mimics how humans localize themselves on maps using spatial layout of semantic elements on the map. Unlike other matching and localization methods that use visual appearance or feature matching, our method relies on robust and consistently detectable semantic elements that are invariant to illumination, temporal variations and occlusions. We use the buildings on the map and on the given aerial query image as our semantic elements. Spatial relations between these elements are efficiently stored and queried under a hierarchical semantic version of the Geometric Hashing algorithm that is inherently rotation and scale invariant. We also present a method to obtain building locations from a given query image using image classification and processing techniques. Overall this approach provides fast and robust localization over large areas. We show our experimental results for localizing satellite image tiles from a 16.5 km sq dense city map with over 7,000 buildings.