Visual-SLAM for Humanoid Robots

Abstract

In robotics the Simultaneous Localization and Mapping (SLAM) is the problem in which an autonomous robots acquires a map of the surrounding environment while at the same time localizes itself inside this map. In the last years a lot of researchers have spent a great effort in developing new families of algorithms, using several sensors and robotic platforms. One of the most challenging field of research in SLAM is the so called Visual-SLAM problem, in which various types of cameras are used as sensor for the navigation. Cameras are inexpensive sensors and can provide rich information about the surrounding environment, on the other hand the complexity of the computer vision tasks and the strong dependence on the characteristics of the environment in current approaches makes the Visual-SLAM far to be considered a closed problem. Most of the SLAM algorithm are usually tested on wheeled robot. These platforms have become robust and stable, on the other hand the research in robot design moves toward a new family of robot platforms, the humanoid robots. Just like humans, a humanoid robot can adapt itself to changes in the environment in order to efficiently reach its goals. Despite that, only a few roboticists focused theirs research on stable implementation of SLAM and Visual SLAM algorithms well suited for humanoid robots. Humanoid platforms raise issues which can compromise the stability of the conventional navigation algorithms, especially for vision-based approaches. A humanoid robot can move in 3D without the usual planar motion assumption that constraint the movement in 2D, usually with quick and complex movements combined with unpredictable vibrations, compromising the reliability of the acquired sensors data, for example introducing in the images grabbed by the camera an undesired motion blur effect. Due to the strong balance constraints, a humanoid robot usually can’t be equipped with powerfull but hefty computer boards: this limits the implementation of complex and computational expensive algorithms. Moreover, unlike wheeled robots, its complex kinematics usually forbids a reliable reconstruction of the motion from the servo-motor encoders. In this thesis, we focus on studying and developing new techniques addressing the Visual-SLAM problem, with particular attention to the issues related to using as experimental platform small humanoid robots equipped with a single perspective camera. The main efforts in SLAM and Visual SLAM research areas have been put into the estimation functionality. However, most of the functionalities involved in Visual SLAM are in perception processes. In this thesis we therefore focus on the improvement of the perceptual processes, from a computer vision point-of-view. We faced small humanoid robot related issues like low-computational capability, the low quality of the sensor data and the high degrees of freedom of the motion. We cope with the low computational resources presenting a new similarity measure for images based on a compact signature to be used in image-based topological SLAM problem. The motion blur problem is faced proposing a new feature detection and tracking scheme that is robust even to non-uniform motion blur. We develop a framework for visual odometry based on features robust to motion blur. We finally propose an homography-based approach to 3D visual SLAM, using the information provided by a single camera mounted on a humanoid robot, based on the assumption that the robot moves on a planar environment. All proposed methods have been validated with experiments and comparative validation using both standard datasets and images taken by the cameras mounted on walking small humanoid robots.