本文为美国加州理工学院（作者：Kristopher L. Kriechbaum）的博士论文，共146页。

移动机器人的自主定位能力是可靠的远程自主导航的基本要求。本文介绍了新的机器人定位、导航工具和算法。介绍了一种新的距离扫描匹配方法，该方法结合了真实的传感器噪声模型。这种方法可以看作是里程计的一种改进形式。结果表明，与典型的移动机器人里程计相比，具有一个数量级的改善。此外，还创建了一个新的基于传感器的规划算法，在该算法中，无论路径是否朝向或暂时远离目标，机器人都无一例外地沿着局部最优路径到达目标。实现代价定义为路径长度。这个新算法称之为\Optim-Bug，是完整且正确的。最后，我开发了一个新的在线运动规划程序，该程序确定了一条通往目标的路径，从而使机器人能够在目标处进行自我定位。这种算法被称为“\Uncertain Bug”。特别是，机器人在目标位置的姿态估计协方差最小化。这一特性增加了机器人实际能够达到预期目标的可能性，即使不确定性破坏了机器人在路径上移动时的定位。机器人的路径是已知的，因此可以利用它的路径来改进它的定位。本文是将移动机器人定位和映射工具与基于传感器的运动规划思想结合起来的第一步。 

The ability for a mobile robot to localize itself is a basic requirement for reliable long range autonomous navigation. This thesis introduces new tools and algorithms to aid in robot localization and navigation. I introduce a new range scan matching method that incorporates realistic sensor noise models. This method can be thought of as an improved form of odometry. Results show an order of magnitude of improvement over typical mobile robot odometry. In addition, I have created a new sensor-based planning algorithm where the robot follows the locally optimal path to the goal without exception, regardless of whether or not the path moves towards or temporarily away from the goal. The cost of a path is defined as the path length. This new algorithm, which I call \Optim-Bug," is complete and correct. Finally, I developed a new on-line motion planning procedure that determines a path to a goal that optimally allows the robot to localize itself at the goal. This algorithm is called \Uncertain Bug." In particular, the covariance of the robot's pose estimate at the goal is minimized. This characteristic increases the likelihood that the robot will actually be able to reach the desired goal, even when uncertainty corrupts its localization during movement along the path. The robot's path is chosen so that it can use known features in the environment to improve its localization. This thesis is a first step towards bringing the tools of mobile robot localization and mapping together with ideas from sensor-based motion planning.

1. 引言

2. 项目背景

3. 加权扫描匹配

4. Optim-Bug

5. 路径优化

6. Uncertain Bug

7. 结论

附录A 加权扫描匹配推导

附录B 具体实现细节

附录C 最短路径特性

附录D 有界不确定的自由空间

更多精彩文章请关注公众号：