仅使用距离和方位来查找位置? [英] Find location using only distance and bearing?

问题描述

三角剖分的工作原理是检查您与三个已知目标的夹角.

Triangulation works by checking your angle to three KNOWN targets.

我知道那是亚历山大灯塔，它位于地图上的(X，Y)，就在我90度的右边."针对不同的目标和角度再重复2次.

"I know the that's the Lighthouse of Alexandria, it's located here (X,Y) on a map, and it's to my right at 90 degrees." Repeat 2 more times for different targets and angles.

三边测量通过检查您与三个已知目标的距离来实现.

Trilateration works by checking your distance from three KNOWN targets.

我知道那是亚历山大灯塔，它位于地图上的这里(X，Y)，距离我100米.针对不同的目标和范围再重复2次.

"I know the that's the Lighthouse of Alexandria, it's located here (X,Y) on a map, and I'm 100 meters away from that." Repeat 2 more times for different targets and ranges.

但是这两种方法都依赖于了解您要查看的内容.

But both of those methods rely on knowing WHAT you're looking at.

假设您在森林中，无法区分树木，但是您知道关键树木在哪里.这些树木已被人工选为地标".

Say you're in a forest and you can't differentiate between trees, but you know where key trees are. These trees have been hand picked as "landmarks."

您有一个机器人在那片森林中缓慢移动.

You have a robot moving through that forest slowly.

您是否知道有任何方法可以完全根据角度和范围来确定位置，并利用地标之间的几何形状?请注意，您还将看到其他树，因此您将不知道哪些树是关键树.忽略目标可能被遮挡的事实.我们的前置算法可以解决这个问题.

Do you know of any ways to determine location based solely off of angle and range, exploiting geometry between landmarks? Note, you will see other trees as well, so you won't know which trees are key trees. Ignore the fact that a target may be occluded. Our pre-algorithm takes care of that.

1)如果存在，那叫什么?我什么都找不到.

1) If this exists, what's it called? I can't find anything.

2)您认为拥有两个相同位置的热门"的可能性是多少?我想这是相当罕见的.

2) What do you think the odds are of having two identical location 'hits?' I imagine it's fairly rare.

3)如果有两个相同的位置命中"，那么接下来移动机器人后如何确定我的确切位置. (我重新定位机器人后，假设连续出现2个完全相同的角度的机会在统计学上是不可能的，除非有像玉米那样成排生长的森林.)我会重新计算一下排名并希望获得最佳成绩吗?还是我会以某种方式将我先前的排名估算值纳入下一个猜测?

3) If there are two identical location 'hits,' how can I determine my exact location after I move the robot next. (I assume the chances of having 2 occurrences of EXACT angles in a row, after I reposition the robot, would be statistically impossible, barring a forest growing in rows like corn). Would I just calculate the position again and hope for the best? Or would I somehow incorporate my previous position estimate into my next guess?

如果存在，那么我想读一读，如果不存在，请将其作为附带项目进行开发.我只是现在没有时间重新发明轮子，也没有时间从头开始实现这一点.因此，如果它不存在，我将不得不找出另一种本地化机器人的方法，因为这不是本研究的目的，如果确实存在，那么希望它会变得简单.

If this exists, I'd like to read about it, and if not, develop it as a side project. I just don't have time to reinvent the wheel right now, nor have the time to implement this from scratch. So if it doesn't exist, I'll have to figure out another way to localize the robot since that's not the aim of this research, if it does, lets hope it's semi-easy.

推荐答案

您正在寻找的是Monte Carlo本地化(也称为粒子过滤器). 这里是有关此主题的好资源.

What you're looking for is Monte Carlo localization (also known as a particle filter). Here's a good resource on the subject.

或几乎所有来自概率机器人技术人群的东西，如Dellaert，Thrun，Burgard或Fox.如果您有雄心壮志，可以尝试使用完整的SLAM解决方案-在此处中发布了许多库.

Or nearly anything from the probabilistic robotics crowd, Dellaert, Thrun, Burgard or Fox. If you're feeling ambitious, you could try to go for a full SLAM solution - a bunch of libraries are posted here.

或者，如果您真的很雄心勃勃，则可以使用因子图.

Or if you're really really ambitious, you could implement from first principles using Factor Graphs.

这篇关于仅使用距离和方位来查找位置?的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！