PoseEstimation | Convolutional Neural Network for Real Time robot pose | Robotics library

PoseEstimation is a Python library typically used in Telecommunications, Media, Advertising, Marketing, Automation, Robotics, Deep Learning applications. PoseEstimation has no bugs, it has no vulnerabilities and it has low support. However PoseEstimation build file is not available. You can download it from GitHub.

Localization is an important issue for navigation, including, self-driving car and indoor navigation for service robots. SLAM has a good performance in indoor localization. Commonly used sensors are mainly divided into lasers or cameras. The advantage of laser SLAM is its high localization accuracy. However, the lack of image information leads to restrictions on some applications, such as finding objects. Visual SLAM relies on RGB image and depth map. It also has good localization performance. Because having RGB images makes it possible to develop more applications in the future. The disadvantage is that a large number of features extracting and matching, cause a large amount of computation. It is easily influenced by missing features, dynamic light sources, and human disturbance. Therefore, this research will focus on the pose estimation only by RGB image, without features extracting and matching. The robot pose is directly regressing by RGB image to achieve the purpose of indoor navigation. In recent years, deep learning and convolutional neural network (CNN) have achieved good results in many computer vision studies. It can train the entire neural network end-to-end and learn features from the data. There have some studies shown that it is possible to use deep learning to estimate pose by RGB images, such as PoseNet and MapNet. In this study, we use laser SLAM to collect the data, including RGB images and robot pose which is used as the training pairs required by PoseNet and MapNet. Our target is to regress the robot pose based on the current RGB image. Finally, apply this system on the real robot Turtlebot3 Waffle Pi, and combined it with path planning and speed control system which develope by ourself to achieve the goal of navigation.