智慧型輪椅機器人基於多感測器融合之遠距離人員偵測與追蹤

Abstract

近幾年因人口老化，如何提升年長者或須以輪椅代步者的自主行動能力，並減輕伴隨者的負擔，即成為十分重要的課題。基於此因，輪椅型機器人增加了智慧型的功能。它提供人員辨識與跟隨的功能，這項功能的加強，使得輪椅不再只是輪椅，而成為最好的輔助工具並陪伴者。本論文提出利用多感測器融合遠距離人員偵測與追蹤的服務。首先本研究使用雷射感測器與慣性感測器配合擴展卡曼濾波器（extended Kalman filter, EKF）來做同步定位與地圖建立（simultaneous localization and map building, SLAM），並且為了增加人員跟隨的強健性，本研究還使用了雷射感測器與影像的感測資訊，一開始利用雷射感測器來偵測人員區間，再使用PTZ攝影機藉由加速穩健性特徵辨識（Speeded Up Robust Feature, SURF）來區別辨識所要跟隨的人員。本系統利用PTZ攝影機能放大焦距與雷射雷達可偵測遠距離的特性，配合所建立的地圖完成遠距離的人員跟隨之功能。僅此，以此論文展示本實驗的成果。

Recently, several robotic wheelchairs have been proposed that employ autonomous functions. In designing wheelchairs, it is important to reduce the accompanist load. To provide such a task, the mobile robot needs to recognize and track people. In this paper, we propose to utilize the multisensory data fusion to track a target accompanist. First, the simultaneous localization and map building is achieved by using the laser range finder (LRF) and inertial sensors with the extended Kalman filter recursively. To track the target person robustly, the accompanist, are tracked by fusing laser and vision data. The human objects are detected by LRF, and the identity of accompanist is recognized using a PTZ camera with a pre-defined signature using the speeded up robust features algorithm. The proposed system can adaptively search visual signature and track the accompanist by dynamically zooming the PTZ camera based on LRF detection results to enlarge the range of human following. The experimental results verified and demonstrated the performance of the proposed system.