Eisuke Terada

Online road segmentation for urban complex environments

In this paper, we propose a novel approach to stable near and long range perception for various complex outdoor environments. Our techniques cope robustly with near-range road estimation using a laser scanner and long-range terrain classification using a color camera. Near-range road surface conditions are estimated by using information of remission value as reflectivity of a laser. We apply graph cut algorithm to grid map in order to estimate road region robustly also in complex environments where fallen leaves exist sparsely. Moreover, we propose superpixel-based terrain classification method which can give a good performance compared with pixel-based classification. Experimental results have shown that demonstrate a marked increase in long-range classification and near-range road estimation accuracy over standard methods.

Near-to-Far Self-Supervised Road Estimation for Complicated Environments

Abstract In this paper, we propose a novel approach to stable near and long range perception for various complex outdoor environments. Our techniques cope robustly with near-range road estimation using a laser scanner and long-range terrain classification using a color camera. Near-range road surface conditions are estimated by using information of remission value as reflectivity of a laser. We apply graph cut algorithm to grid map in order to estimate road region robustly also in complex environments where fallen leaves exist sparsely. Moreover, we propose superpixel-based terrain classification method which can give a good performance compared with pixel-based classification. Our approach has real-time processing. Experimental results have shown that demonstrate a marked increase in long-range classification and near-range road estimation accuracy over standard methods.

Adaptive speed control system using supervised long-range road estimation

In this paper, we propose a speed control system for autonomous mobile robots using long-range road estimation. In our method, the long-range road is estimated using the images from a camera and the result of near-range road estimation from a laser scanner. Near-range road surface conditions are estimated by using information of remission value as reflectivity of a laser. Our speed control determines velocity from the width and distance of the road, and this speed control makes the robots performance more efficient (speedy). Our proposed method is adapted to various environments and it is effective to moving objects. Experimental results show that efficient movement can be performed by our proposed method.