  The visual simultaneous localization and mapping(vSLAM) is widely used in
GPS-denied and open field environments for ground and surface robots. However,
due to the frequent perception failures derived from lacking visual texture or
the {swing} of robot view direction on rough terrains, the accuracy and
robustness of vSLAM are still to be enhanced. The study develops a novel view
planning approach of actively perceiving areas with maximal information to
address the mentioned problem; a gimbal camera is used as the main sensor.
Firstly, a map representation based on feature distribution-weighted Fisher
information is proposed to completely and effectively represent environmental
information richness. With the map representation, a continuous environmental
information model is further established to convert the discrete information
space into a continuous one for numerical optimization in real-time.
Subsequently, the receding horizon optimization is utilized to obtain the
optimal informative viewpoints with simultaneously considering the robotic
perception, exploration and motion cost based on the continuous environmental
model. Finally, several simulations and outdoor experiments are performed to
verify the improvement of localization robustness and accuracy by the proposed
approach.
