Abstract: In the past, an unmanned vehicle had been recognized in the category of national defense industry so that there had been many limits to its application. With the rising public attention to unmanned vehicles, domestic and foreign firms have more invested in unmanned vehicles. With the development of technology and the increased private demands, the unmanned vehicle market is on the sharp rise. Since, an autonomous car of the IT company Google in the private sector was released, Complete Build Up (CBU) firms in Germany and other countries have been launching their autonomous car. Although, some countries allow test-driving of unmanned vehicles, no country institutionalizes unmanned vehicle driving. Existing unmanned vehicles frequently have malfunction of rear sensor and face safety accidents often in unexpected situations like a blind spot of rear camera. When the trivial defects are solved, it is possible to make deregulation for commercializing unmanned vehicles. The domestic industry failed to predict the growth possibility of the unmanned vehicle market so that the development was delayed. Therefore, this study proposes the development method of the unmanned vehicle technology for autonomous avoidance in order to secure competitiveness and enter in the global market in a short period of time. The study subjects are three technologies which include the technology of measuring a frontal distance from an unmanned vehicle camera and recognizing an object in camera image and unmanned vehicle navigation technologies like the autonomous driving technology to enable an unmanned vehicle to arrive at a destination. For research, ‘Kobuki’, the ROS official reference mobile robot, various distance sensors and depth camera are used. The research purpose is to compare the accuracy of the measurement of the distance between various distance sensors and depth camera SLAM (Simultaneous Localization and Mapping) emerged in order for an unmanned vehicle to drive in an unknown space, sense surroundings and create a current position and map for its autonomous avoidance. There are a variety of SLAM algorithms which are used to create a map. The map and a path-finding algorithm are applied for navigation. For a variety of SLAM algorithms, their performance is compared in terms of mapping time and map accuracy. Through performance comparison, an unmanned vehicle uses a map and a path-finding algorithm to avoid obstacles and drive. This study also suggests the performance comparison of path-finding algorithms in terms of the shortest path, driving time and the number of operations.