摘要
由于其恶劣的自然环境,近几年来极地逐渐成为机器人应用的一个典型场景.作为一种代表性的极地机器人,长航程极地漫游机器人对环境建模及环境认知的能力提出了更高的要求.为了提高环境建模方法实时性以及环境模型精度,本文提出了一种能够适用于各种野外环境的多尺度2.5维概率栅格高程图.该高程图从空间尺度对环境进行精细及粗略两种划分以适应不同的环境模型精度要求及建模实时性要求.从概率尺度,该高程图首先采用卡尔曼滤波器融合量测点不确定性对精细栅格高程值进行估计,再采用概率统计对粗略栅格高程值进行估计.同时,本文结合极地环境常见障碍物冰裂隙对环境模型的插值问题进行了研究,提出了基于假设检验的插值方法.最后,本文对提出的环境建模方法及插值方法进行了实验验证.
Due to the harsh environments, polar region is now becoming one of the typical scenarios for mobile robots. As one kind of representative polar robots, the long-range polar rover robots require higher autonomy to perceive surrounding environments. To improve the real-time performance and environment model accuracy of environment modeling of outdoor environments, multi-scale 2.5D probabilistic elevation grids is proposed. From the perspective of space scale, the model lattices the environment into refined and coarse grids to adapt to different model accuracy requirements and real-time requirements. From the perspective of probability, the refined grids are estimated by Kalman filter while the coarse grids are represented by the statistics of the covered refined grids. Meanwhile, interpolation of the environment model based on hypothesis tests with the presence of ice cracks is studied. The experiments validate the effectivity of the proposed environment modelling and interpolation algorithms.
出处
《科学通报》
EI
CAS
CSCD
北大核心
2013年第S2期75-82,共8页
Chinese Science Bulletin
基金
国家自然科学基金重点项目(61035005)资助
关键词
极地环境
自主导航
漫游机器人
环境建模
插值问题
polar environments,autonomous navigation,rover robots,environment modeling,model interpolation
