Multisensor fusion for simultaneous localization and map building

In this paper, we have studied strategies for controlling formations of mobile robots using methods from nonlinear control theory and graph theory. We have focused on decomposing the problem of controlling a formation of nonholonomic mobile robots into: 1) controlling a single lead robot and 2) controlling other follower robots in the team. We used the terms l 0 and l 0 l control to reflect whether the control laws are based on tracking the position and orientation of the robot relative to a leader, or the position relative to two leaders, respectively. We also defined the concept of a transition matrix, which governs the addition and deletion of edges in the control graph and hence the change in the communication protocol. Based on this, we presented an exhaustive list of all possible transitions that can occur within the robots in the formation and the corresponding transition matrix column. There are several important issues that need to be addressed in future research in this area, including: 1) how to choose a control graph and the desired shape based on the constraints in the environment; 2) how to plan changes in (g; r; H) depending on sensor constraints; 3) how to allow formations to be split into sub-formations, leading to multiple lead robots; and 4d) though the transition matrix gives us the information needed to change formations, it is not clear if there is an optimal way for carrying out these changes, rather than the sequential algorithm presented here. Some of these topics are the focus of our present research. A motion planner for nonholonomic mobile robots, " IEEE Trans. [6] O. Khatib, " Real-time obstacle avoidance for manipulators and mobile robots, " Int. Abstract—This paper describes how multisensor fusion increases both reliability and precision of the environmental observations used for the simultaneous localization and map-building problem for mobile robots. Mul-tisensor fusion is performed at the level of landmarks, which represent sets of related and possibly correlated sensor observations. The work emphasizes the idea of partial redundancy due to the different nature of the information provided by different sensors. Experimentation with a mobile robot equipped with a multisensor system composed of a 2-D laser rangefinder and a charge coupled device camera is reported.