Cyril Joly

Building consistent local submaps with omnidirectional SLAM

Autonomous and safe robot navigation requires the capability to simultaneously building a map of the environment and a selflocalization of the robot itself. This is known as the SLAM (Simultaneous Localization and Mapping) problem. In such a context, omnidirectional camera looks like a very interesting sensor since it allows a full 360 degrees field of vision. Complexity of the SLAM methods dramatically increases when the size of the environment grows up. Conversely, accuracy and integrity of the estimation process cannot be guaranteed any more. In this paper, we present an efficient way to reduce the complexity of the algorithms thanks to the definition of local submaps. In contrast with other papers dealing with local maps, our main purpose is not only to reduce the computational cost but also to keep the global map correlated. A method to consistently share information between local maps is also provided. These methods are used in the context of the omnidirectional bearing-only SLAM. Due to the specificity of the model of projection, images provided by the omnidirectional cameras cannot be used in a SLAM method without taking some precaution. In this paper, we provide an original method for computing the covariance matrix taking into account this specificity. Finally, we provide a validation with real data in an indoor environment subject to large illumination changes.

Self calibration of a vision system embedded in a visual SLAM framework

This paper presents a novel approach to self calibrate the extrinsic parameters of a camera mounted on a mobile robot in the context of fusion with the odometry sensor. Calibrating precisely such a system can be difficult if the camera is mounted on a vehicle where the frame is difficult to localize precisely (like on a car for example). However, the knowledge of the camera pose in the robot frame is essential in order to make a consistent fusion of the sensor measurements. Our approach is based on a Simultaneous Localization and Mapping (SLAM) framework: the estimation of the parameters is made when the robot moves in an unknown environment which is only viewed by the camera. First, a study of the observability properties of the system is made in order to characterize conditions that its inputs have to satisfy to make the calibration process possible. Then, we show on a real experiment with an omnidirectional camera the validity of the conditions and the quality of the estimation of the 3D pose of the camera with respect to the odometry frame.