Research On Multi-sensor Data Fusion Based Pose Calculation Of Mobile Robot

With the development of computer science, sensor technology, artificial intelligence and the improvement of manufacture level, the robotics increasingly tends toward intelligent and autonomous. In order to make the robot move in the environment autonomously and do the task, the robot must be capable of calculating its pose. Pose calculation or localization problem is a key researching domain in the mobile robot community and get much attention around the world.This dissertation is focused on the multi-sensor fusion based pose calculation problem for mobile robot. The intention of this dissertation is to describe the research on encoder, laser rangefinder and vision data processing and fusion, multi-robot pose calculating and tracking problem in fixed environment and the mapping based localization problem for autonomous robot. The main contents and contributions of this dissertation include the following aspects:In pose calculation of mobile robot, the original range image from Laser rangefinder appears at non-uniform scale or resolution in scenery, which causes false alarms and missed detections. An adaptive smoothing algorithm within a scale space framework is introduced for noisy range image of laser rangefinder in order to extract features. Then the features can be segmented and identified according to the curvature of the range data, which decrease the false alarms and missed detections. Experimental results show that the proposed method is efficient in feature extraction,which can improve the accuracy and robustness of robot pose calculation. When mobile robot working in dynamic environment, the original vision image has the disadvantage of distortion and contains disturbing features, which lead to difficulty for robot pose calculation. In order to solve the problem, a flexible camera model contained radial and tangential distortion is established to correct the distorted images. Then this paper uses a recognition algorithm combined color segmentation with recognition method based on a shape template, which effectively reduce misidentification and improve the robustness of robot recognition. Then, a prediction algorithm based on the model of mobile robot is presented. This method can predict pose state of robot in the next frame and reduce the searching area of image, which guarantee the real-time performance for the pose calculation.Multi-sensor fusion can improve the accuracy as well as the robustness of the pose calculation for mobile robot. In order to calculate the poses of several robots, a distributed multi-sensor fusion pose calculation method is proposed. The measured data from the vision system and laser rangefinder is matched and correlated with the robots in the environment by data association process, and are combined with the information from encoder by a two layer UKF on robot. The distributed framework takes the advantage of high flexibility, and does not limit to the number of tracking robots. Experimental results show that the proposed method has high accuracy of robot pose measuring and strong stability.In order to improve the precision and reduce the sample impoverishment problem of autonomous localization based on Particle Filter, an improved Rao-Blackwellized Particle Filter by incorporating the most recent sensor observation is proposed. The filter uses Minimal Skew Unscented Kalman Filter (MS-UKF) to generate proposal distributions in order to optimize the samples, which can obtain satisfying calculation results with a small sample set. Moreover, we propose an MS-UKF based assistant-proposal distribution during resampling, which keeps the diversity and randomness. A series of experiments are carried out on a mobile robot, and the results show that the method effectively improves the precision and efficiency of robot pose calculation.The contribution of the dissertation consists in improving the capability of environment perception, co-operating and autonomous navigation of mobile robot. This is of positive academic significance and practical importance to improve the quality and wide the application field of mobile robot.