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机器人学习--Robotics: Estimation and Learning（宾夕法尼亚大学COURSERA课程）

發布時間：2025/3/13 编程问答 16 豆豆

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COURSERA網課網址：https://www.coursera.org/learn/robotics-learning#syllabus

總共四周課程，四個模塊內容。

博客園網友截圖及學習筆記：https://www.cnblogs.com/ecoflex/p/9868474.html

?https://blog.csdn.net/jinshengtao/article/details/81294285

?https://www.getit01.com/p2018012421648507/

converge to a local optima

The robot will not directly measure X unfortunately, but

the robot may observe portions of x through it's sensors.

This portion is labeled z, where the relationship between the state and

measurement is given by the mixing matrix, c.

Creditly both X and Z contain noise even in this model.

State X is noisy because the linear model does not capture all

physical interactions.

Observation Z are noisy because sensors contain noise in their measurements.

In the next section, we will show how these two probability distributions

can be fused to provide a better estimate of the true state.

From the dynamical system, the probability of the state given only the previous

state can be represented with the prior information?alpha.

Representing the information from our measurement model,

beta?provides observational evidence.

building a semantic map requires

advanced object recognition techniques which go beyond our scope.

But we are most interested in the range sensor it has on the top.

?https://www.cnblogs.com/flash3d/archive/2012/01/30/2332121.html

?https://www.quora.com/Why-do-self-driving-cars-use-LIDAR-instead-of-depth-cameras-like-Kinect

?https://www.zhihu.com/question/28749424

k-d tree算法

https://www.cnblogs.com/eyeszjwang/articles/2429382.html

Here, theta encompasses 270 degrees, not a full circle.

The laser scanner can only see 10 to 30 meters away.

In this range restriction, means that distance measurements

showing here is black dots, can only be found within the area in green.

The complimentary stages of mapping and localization when performed together

are known as SLAM, simultaneous localization and

mapping, which is a major research topic in robotics.

we have some tricks to make the search easier.

We can constrain the search to a limited number of poses?based on odometry information.

http://www.cnblogs.com/maybe2030/p/5043356.html

?https://blog.csdn.net/heyijia0327/article/details/40899819

?https://www.cnblogs.com/21207-iHome/p/5237701.html

?? ?

so, we need to find a rotation and?translation that move the measured points to match the model points.

?? ? ? ??

The strategy of the ICP algorithm takes an optimistic?assumption that the point sets are close enough.

?? ? ??

知乎上網友的學習筆記：https://zhuanlan.zhihu.com/p/21648507?refer=robotics-learning

建圖部分的網友解析：https://www.cnblogs.com/ecoflex/p/9868474.html

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