This paper presents a method to incorporate 3D line segments in vision based SLAM. A landmark initialization method that relies on the Plucker coordinates to represent a 3D line is introduced: a Gaussian sum approximates the feature initial state and is updated as new observations are gathered by the camera. Once initialized, the landmarks state is estimated along an EKF-based SLAM approach: constraints associated with the Plucker representation are considered during the update step of the Kalman filter. The whole SLAM algorithm is validated in simulation runs and results obtained with real data are presented.

In the classic EKF SLAM framework, the vision based approaches raise the problem of landmark initialization. As a single observation is not enough to compute a gaussian state estimate, a special initialisation algorithm must be developed. This video presents our approach with a step by step simulation, then a more complex simulation is proposed. The last part gathers results obtained on real data.

This article presents a bearing only 3D SLAM algorithm which has the same complexity and optimality as the usual extended Kalman filter used in classical SLAM. We especially focus on the landmark initialization process, which relies on visual point features tracked in the sequence of acquired images: a probabilistic approach to estimate their parameters is presented. This induces a particular structure of the filter architecture, in which are memorized a set of past robot poses. Simulations are made to compare the influence of some parameters required by our approach, and results with an indoor robot and an airship are presented.

Most solutions to the SLAM problem in robotics have utilised range and beating sensors as the provided perception data is easy to incorporate, allowing immediate landmark initialization. This is not the case when using bearing-only information because the distance to the perceived landmarks is not directly provided. A whole estimate of a landmark position is only possible via a set of measurements taken from different points of view. The vast majority of contributions to this problem perform a parallel task to get this estimate, and hence the landmark initialization is delayed. We give a new insight to the problem and present a method to avoid this delay by initializing the whole ray that defines the direction of the landmark. We utilize a minimal and computationally efficient form to represent this ray and a new strategy for the subsequent updates. Simulations have been carried out to validate the proposed algorithms.

This paper deals with the task allocation problem in multi-robot systems. We propose a completely distributed architecture, where robots dynamically allocate their tasks while they are building their plans. We first focus on the problem of simple "goto" tasks allocation: our approach involves an incremental task allocation algorithm based on the Contract-Net protocol. We introduce a parameter called equity coefficient in order to equilibrate the workload between the different robots and to control the triggering of the auction process. Then, we address the problem raised by temporal constraints between tasks by dynamically specifying temporary hierarchies among the tasks. Tests run in simulation quantify the benefits of our improvements.