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.

翻译：视觉同时定位与地图构建(vSLAM)广泛应用于地面及地表机器人在无GPS信号的开阔场景中。然而，由于视觉纹理缺失或机器人因崎岖地形导致视角摆动引发的频繁感知失效，vSLAM的精度与鲁棒性仍有待提升。本研究提出了一种主动感知最大信息区域的视角规划方法以解决上述问题，并采用云台相机作为核心传感器。首先，提出基于特征分布加权Fisher信息量的地图表征方法，用以完整且高效地描述环境信息丰富度。在此基础上建立连续环境信息模型，将离散信息空间转化为连续空间，从而实现数值优化。随后，基于该连续环境模型，采用滚动时域优化方法获取最优信息视角，同时兼顾机器人感知、探索与运动代价。最后，通过仿真与户外实验验证了所提方法在提升定位鲁棒性与精度方面的有效性。