Mobile Robot Homing Based On Panoramic Vision

Navigation is the key technology of achieving robot intelligence and autonomous mobile. Visual navigation can detect a wider range and get much more information, that is important to research mobile robot in the future. However, the precise model of environment must be established in traditional navigation methods. For example, SLAM(simultaneous localization and mapping) has a higher space complexity and more difficult extended application. Visual homing is a method of behavior-based navigation, which has been inspired by the principle of biological navigation. These methods change the simultaneous localization and mapping to determine the running direction and stop position. So it is more similar to intelligent life navigation. Visual homing does not need an accurate environmental model, and it occupies less storage space.. There is no accumulation error during navigation, so the accuracy is not affected from the distance. This paper deeply studied robot visual homing based on natural landmark used panoramic vision sensor.First, we studied on extracting natural landmark from the panoramic image. Because there are not artificial landmarks in the unstructured environment, so the robot can only rely on the natural landmarks of environment to achieve homing. Comparing a variety of methods of extracting natural landmark, local feature has many invariance and it is suitable as natural landmark points. SIFT and SURF are two efficient and robust spot extracting methods, so more and more scholars began to focus on them. But their performances in panoramic image had not been evaluated. In order to obtain stable natural landmark points, we evaluated the performance of SIFT and SURF using repeatability, match rate and mismatch rate. At the same time, a method of evaluating distribution uniformity of feature points has been proposed. And we assessed the distribution uniformity of SIFT and SURF feature points in different environment.Second, we calculated relevance of natural landmarks. Because mismatch will interfere with deviating from right direction during homing and even failed homing. So the matched natural landmarks must be purified. If using the previous matching results, there will be many landmarks matching to one landmark. And it is incorrect or unstable. In this paper, an improved matching method to solve this problem has been proposed. To solve the mismatching problem in panoramic image, two methods of eliminating mismatches have been also presented in this paper, which were based on angle estimation and the longest common subsequence. After completion of extraction, matching, purification of natural landmarks, the visual homing method based on natural landmarks were given, which included average displacement vector method, average landmark vector method. And the homing method based on angle difference was firstly completely described. Homing experiment system was constructed and moving track of robot was recorded by dead reckoning method. To improve the positioning accuracy, the robot tracked skid was compensated and azimuth error was corrected using angle estimation based on panoramic vision. We compared of ADV, ALV and the included angle difference based on landmarks in our test environment. Because ADV,ALV must have known(should know) the azimuth of home position, we reduced the constraints by angle estimation.Finally, we simply studied the long range visual homing. We increased the number of intermediate home targets, and used multiple intermediate nodes to guide the robot to reach the last home position. Intermediate nodes are organized in the form of a topological map, and the robot can automatically creates a topological map after leaving home position.