1A1-I01 クラスタリング手法を用いた物体位置推定と学習型画像認識の融合による静止障害物検出手法の提案

抄録

We propose the method to detect a stationary obstacle in the environment by a laser range finder and a monocular camera.The method is divided into two systems, the estimation of object system and the image recognition system.The system for estimation of object position uses k- means clustering, and improves disadvantage of k-means clustering that does not guarantee to split clusters optimally depending on the situation.The system for image recognition uses the method of Bag of Features to represent image features and the method of Support Vector Machine in classification.Both systems are run multi-threaded, maintain the real-time properties.Then, we evaluate the performance of each system and confirm behavior when both systems are combined.