TwoView Fusion based Convolutional Neural Network for Urban Road Detection In this paper, we propose a twoview fusion based convolutional neural network to estimate road areas in urban environments with LiDAR point clouds as input only. The proposed network takes two transformed LiDAR data representations, the LiDAR imageries and the cameraperspective maps, as inputs. It outputs pixelwise road detection results in both the LiDARxe2x80x99s imagery view and the cameraxe2x80x99s perspective view simultaneously, in an endtoend manner. To make better use of the data associations between two representations, we construct a novel mapping layer to transform features from the LiDARxe2x80x99s imagery view to the cameraxe2x80x99s perspective view in order to strengthen the road detection performance in the cameraxe2x80x99s perspective view. Experiments on the KITTIRoad dataset show that the proposed network can achieve the stateoftheart performance among all LiDARonly methods in real time.

Fusing Lidar Data and Aerial Imagery with Perspective Correction for Precise Localization in Urban Canyons This paper addresses a vehicle localization method that fuses aerial maps and lidar data in urban canyon environments where global positioning system (GPS) signals are inaccurate. The boundaries of buildings are extracted from the aerial map and they are matched to point cloud data provided by the lidar. However, most aerial maps contain perspective projection distortions which can be significant in urban canyons with tall buildings. In this study, a new method to correct such projection distortion is proposed and it is applied to precise localization by fusing the corrected map and lidar data. In order to achieve this, the semantic segmentation of an aerial image is performed using a convolutional neural network, and the mutual information between the lidar measurements and the building boundaries is obtained to measure their similarity. A particle filter framework is employed to localize the vehicle and match the map using the mutual information as the weight of a particle. An experimental dataset is then used to validate the feasibility of the proposed method.