Efficient Information-based Visual Robotic Mapping in Unstructured Environments
Efficient Information-based Visual Robotic Mapping in Unstructured Environments
复制标题
非结构化环境中基于信息的高效视觉机器人测绘
DOI：
10.1177/0278364905051774
复制
发表时间：
2005
期刊：
影响因子：
--
通讯作者：
S. Dubowsky
中科院分区：
文献类型：
--
作者：
V. Sujan;S. Dubowsky
在野外环境中，通常不可能为机器人团队提供详细的先验环境和任务模型。在这样的非结构化环境中，机器人将需要通过执行适当的传感器操作来创建其周围环境的尺寸精确的三维几何模型。然而，机器人位置的不确定性和传感限制/遮挡使这一点变得困难。提出了一种基于迭代传感器规划和传感器冗余的新算法，为具有铰接式传感器的移动机器人构建几何一致的环境空间地图。其目的是获得新的信息，从而对环境有更详细、更完整的了解。机器人(S)被控制以最大限度地利用基于香农信息论的评估函数获得关于其环境的几何知识。使用未知环境的测量和马尔可夫预测，基于信息论的度量被最大化以确定机器人代理对环境的下一个最佳视图(NBV)。使用卡尔曼滤波统计不确定性模型将在该NBV姿态下收集的数据融合到测量的环境地图中。该过程将继续进行，直到环境映射过程完成。这项工作在应用信息论来提高环境感知机器人代理的性能方面是独一无二的。它可以被多个分布式和分散式感知代理用于高效和准确的协作环境建模。该算法不对环境结构作任何假设。因此，由于所建立的环境模型不依赖于任何单个智能体框架，而是设置在绝对参考系中，因此它对机器人故障具有鲁棒性。它考虑了传感不确定性、机器人运动不确定性、环境模型不确定性等关键参数。它允许更高兴趣的区域受到代理商的更大关注。该算法特别适用于传感器不确定性和遮挡严重的非结构化环境。仿真和实验证明了该算法的有效性。
In field environments it is often not possible to provide robot teams with detailed a priori environment and task models. In such unstructured environments, robots will need to create a dimensionally accurate three-dimensional geometric model of its surroundings by performing appropriate sensor actions. However, uncertainties in robot locations and sensing limitations/occlusions make this difficult. A new algorithm, based on iterative sensor planning and sensor redundancy, is proposed to build a geometrically consistent dimensional map of the environment for mobile robots that have articulated sensors. The aim is to acquire new information that leads to more detailed and complete knowledge of the environment. The robot(s) is controlled to maximize geometric knowledge gained of its environment using an evaluation function based on Shannon’s information theory. Using the measured and Markovian predictions of the unknown environment, an information theory based metric is maximized to determine a robotic agent’s next best view (NBV) of the environment. Data collected at this NBV pose are fused using a Kalman filter statistical uncertainty model to the measured environment map. The process continues until the environment mapping process is complete. The work is unique in the application of information theory to enhance the performance of environment sensing robot agents. It may be used by multiple distributed and decentralized sensing agents for efficient and accurate cooperative environment modeling. The algorithm makes no assumptions of the environment structure. Hence, it is robust to robot failure since the environment model being built is not dependent on any single agent frame, but is set in an absolute reference frame. It accounts for sensing uncertainty, robot motion uncertainty, environment model uncertainty and other critical parameters. It allows for regions of higher interest receiving greater attention by the agents. This algorithm is particularly well suited to unstructured environments, where sensor uncertainty and occlusions are significant. Simulations and experiments show the effectiveness of this algorithm.