Active global localization based on localizability for mobile robots

SUMMARYIn global localization under the framework of a particle filter, the acquiring of effective observations of the whole particle system will be greatly effected by the uncertainty of a prior-map, such as unspecific structures and noises. In this study, taking the uncertainty of the prior-map into account, a localizability-based action selection mechanism for mobile robots is proposed to accelerate the convergence of global localization. Localizability is defined to evaluate the observations according to the prior-map (probabilistic grid map) and observation (laser range-finder) models based on the Cramér-Rao Bound. The evaluation considers the uncertainty of the prior-map and does not need to extract any specific observation features. Essentially, localizability is the determinant of the inverse covariance matrix for localization. Specifically, at the beginning of every filtering step, the action, which makes the whole particle system to achieve the maximum localizability distinctness, is selected as the actual action. Then there will be the increased opportunities for accelerating the convergence of the particles, especially in the face of the prior-map with uncertainty. Additionally, the computational complexity of the proposed algorithm does not increase significantly, as the localizability is pre-cached off-line. In simulations, the proposed active algorithm is compared with the passive algorithm (i.e. global localization with the random robot actions) in environments with different degrees of uncertainty. In experiments, the effectiveness of the localizability is verified and then the comparative experiments are conducted based on an intelligent wheelchair platform in a real environment. Finally, the experimental results are compared and analyzed among the existing active algorithms. The results demonstrate that the proposed algorithm could accelerate the convergence of global localization and enhance the robustness against the system ambiguities, thereby reducing the failure probability of the convergence.