跟读练习: Turn a Time-of-Flight Sensor into a 3D Scanner - 通过视频学习英语口语

选择练习模式

每种模式都会打开全屏练习室。

139 句

如果句子过短或过长，请点击 Edit 进行调整。

Today I want to show you a little setup that I've got here on the breadboard.

These are all parts from Aliexpress so it's a relatively cheap setup.

The ESP cost me about £4, the time of flight sensor cost me about £5 and this IMU was about £15.

So it's the most expensive part of the system and if we wanted we could replace it with a cheaper alternative.

So this whole setup cost about £24, that would be $30 or €28.

Eros.

So the reason why this tiny LiDAR is so special is that it is a multi -zone time -of -flight sensor.

So what that means is that instead of shooting out a single ray

and measuring the distance at which that ray bounces off an obstacle, it shoots out a 8x8 grid.

So those are 64 rays coming out of it that will bounce back once they hit an obstacle.

Now I've connected the breadboard to my computer via a USB cable and the LED is on so we know it's powered.

Here in the viewer now we can see that we've got roughly the same layout for the two breakout boards.

We have the IMU in purple and the time -of -flight sensor in green.

Here on the right we have the status panel showing the live data

and we can see that the IMU is connected

and we're getting measurement data from the time -of -flight at about 15 Hz.

Now you see these blue rays shooting out from the time -of -flight sensor.

There are 64 of them arranged in an 8x8 grid and each represents one zone that the sensor can measure.

Notice how they don't start right at the sensor, there's a small gap.

And they extend outward, but not infinitely.

If I zoom out, you can see that.

These line segments span the sensor's measurable range from 2 cm to 4 m.

Now, this is fundamentally different from how cameras work.

A camera captures light across a continuous image, but the sensor, it can only measure along these specific rays.

The points you see here, they're locked to these line segments.

They cannot appear anywhere else

and you can see as I slide my hand across the sensor you see the points going left

and right and also back and forth

and I can also hold my hand like this

and just slide my finger underneath it

and you can see there's a wave changing the points from the from the palm of my hand

now here's where it gets interesting let me zoom out

and we will see the points here that are projected onto the ceiling of the room where I'm at.

And you see in the settings panel this option here for clipping to measurement.

Let me enable it and let's see what happens.

Do you see how the rays now stop at the measure points instead of extending to the maximum range?

This is a much more accurate representation of what's actually physically happening.

The sensor shoots out infrared laser pulses.

They travel along these paths, bounce off of whatever obstacle they hit, and reflect back.

The sensor measures how long that took, that's the time of flight, and calculates the distance.

So when I hover my hand over the sensor, you can see some rays clip at my hand while others continue to the ceiling behind it.

And that's exactly what's happening in the real world.

Some laser pulses are hitting my hand, others are missing it and hitting the ceiling.

I think this visualization does a much better job of conveying what this sensor does compared to some 2D heatmaps

that you typically see online.

And it's a lot of fun to play with.

Alright, I've zoomed back in because I want to talk a bit about this purple breakout board here.

It's an IMU.

And watch what happens in a visualizer when I rotate the breadboard in the real world.

It also rotates.

The IMU on the breadboard is tracking the orientation in real time

and since both the IMU

and the time -of -flight sensor are stuck to the same breadboard we always know which direction the sensor is pointing.

Let me zoom back out and you can kind of see how if I point around I

can measure different points around my room.

Let's place the breadboard back on the table you may notice

that the points are a bit jittery

and that's just sensor noise from radiation coming from the window or noise from the actual sensor measurement.

Now if I come over here to the side panel

and enable that filter you can see how the noise kind of smooths out

and I can control the strength of the filter with this slider.

What's happening under the hood is an exponential moving average.

At 0 .5 strength, each new position is calculated as 50 % of the new measurement plus 50 % of the previous position.

So it's essentially averaging the current and previous readings.

If I crank up the strength to 0 .9, it gets even smoother.

But now there's a noticeable lag.

So when I move this two -point from the ceiling to the wall, you can see that the points are really smooth,

but there's a noticeable delay.

At 90 % previous and only 10 % new measurement, the visualization can't keep up with the fast movements.

So it's a trade -off between smoothness and responsiveness.

If I move the strength all the way down to 0, that essentially means there is no filter.

It's the same as if this checkbox was left unchecked.

And watch what happens if I move all the way to 1.

That means that the previous measurement gets all the weight and 0 % is assigned to the new measurement.

So even though I'm rotating the breadboard around the room, you see that no new measurements are coming in.

Let me move the slider back to 0 .5, which I found to be a good strength for the filter.

Alright, let's move on to the next section.

The viewer can also fit the plane to the measured points.

When I enable plane fitting with the least squares method and point the sensor at a flat surface like the ceiling,

you can see if it's a nice plane.

But watch what happens when I point the sensor at both the wall and the ceiling at the same time.

The fitted plane ends up diagonally, trying to minimize the error across all points, and this is not very useful.

This is where RANSAC comes in.

RANSAC stands for Random Sample Consensus.

When I switch to this method, watch how it behaves differently.

RANSAC works by randomly picking three of these sampled points, the minimum to define a plane.

Fitting a plane to those three, then checking how many other points lie close to that plane,

and it repeats this about a hundred times and keeps the best fit.

The key difference is, instead of trying to please all the points equally,

Ransack finds the plane that the majority of points agree on and ignores the outliers.

So now when I point again at both the ceiling and the wall, it snaps to whichever surface has more points.

Rather than compromising with a meaningless diagonal.

And so even if I come in with a pen to disturb the sensor measurements,

you can see how it still is able to be robust to that and fit the ceiling nicely.

And if I change to least squares,

you see how much the disturbance from waving this pen on top of the sensor affects the fitted plane.

I'm going to change again to RANSACK now.

If you cover enough points you can see that it still affects the plane fitting algorithm,

but most of the time it behaves much better than least squares.

100

I've changed to least squares again.

101

I'm just hovering the pen back and forth and I'm gonna change to RANSACK again.

102

It's a really big difference.

103

Up until now we've been showing the current snapshot seen by the sensor.

104

In other words, the viewer is only showing the points that the sensor sees right now at any given point in time.

105

When new measurements come in, the old points disappear and that's why it is so interactive.

106

Mapping mode changes that.

107

Instead of replacing points, it accumulates them.

108

So watch what happens when I hover my hand over the sensor with mapping mode enabled.

109

You can see points starting to build up in the shape of my hand's movement,

110

and covering most of the sensors point field of view up close almost like a truncated pyramid.

111

Okay I've zoomed back out and cleared them up.

112

Here's where the magic really happens.

113

You remember we have the IMU tracking orientation right?

114

So watch what happens when I rotate the breadboard.

115

Look at that!

116

The accumulated points are building up a map of the room so I can see the walls, the ceiling, my monitor, my desk,

117

if I go under the desk I can see the floor

118

and if I point at myself you'll even be able to see me

119

and I can even put my left arm up

120

and then my right arm up and

121

because we're no longer just showing the instantaneous points

122

that the sensor is measuring we've just accumulated them we essentially have a map of the room we have a mapping device

123

Now, the reason why this works is because the IMU knows exactly

124

which direction the sensor is pointing at as I rotate it, and each measurement gets placed in the correct position in 3D space,

125

and they all accumulate into a coherent point cloud.

126

This is really cool!

127

With just an ESP32, a time -of -flight sensor, and an IMU, we've essentially built a simple 3D mapping device.

128

Now there's one limitation worth mentioning.

129

The IMU is excellent at measuring rotation, but it cannot accurately track translations, moving the sensors side to side or forward and back.

130

So if I walk around with a breadboard, the map will drift and become inaccurate.

131

For best results, I always try to rotate the sensor in place without translating too much, just like this.

132

Within that constraint, you get surprisingly good results from such a simple and cheap setup.

133

And that's it!

134

This concludes the video.

135

I think this demo really shows how powerful the combination of a multi -zone time -of -flight sensor

136

and an IMU can be.

137

And for around $30 in parts, you can build something that gives you a real intuition for what the sensors are actually measuring

138

and could be useful for a robotics project.

139

Thanks for watching and I'll see you in the next one!

下载应用

流利口语所需的一切

AI 发音评分逐句打分

音标练习掌握每个音

词汇学习建立你的词库

词汇游戏边玩边学

扫码

Download on theApp Store Get it onGoogle Play

FOR YOU

本课的词汇与口语要点

这节口语课以视频“Turn a Time-of-Flight Sensor into a 3D Scanner”为素材。视频中反复出现的词有：sensor, Imu, measurement, plane, breadboard。这段视频共有 42 个句子、1785 个单词可供跟读。讲话部分时长为 11:19。说话人语速自然，每分钟约 158 个词，接近日常对话。只有 78% 的单词属于英语最常用的 3,000 词，词汇难度较高。

视频中的重点词汇

视频中 15 个值得学习的单词，附发音和释义：

单词	发音	释义
sensor 名词	/ˈsɛn.sɚ/	傳感器 /传感器
measurement 名词	/ˈmɛʒ.ə.mənt/	尺寸
ceiling 名词	/ˈsilɪŋ/	天花板, 天棚
rotate 动词	/ˈɹoʊ.teɪt/	旋转, 转动
essentially 副词	/ɪˈsɛnʃəli/	本質上 /本质上, 基本上
filter 名词	/ˈfɪltɚ/	過濾器 /过滤器, 濾過器 /滤过器
smooth 形容词	/smuːð/	平穩 /平稳, 順利 /顺利
viewer 名词	/ˈvju.ɚ/	觀眾 /观众, 看客
obstacle 名词	/ˈɑbstəkəl/	障礙 /障碍, 妨礙 /妨碍
desk 名词	/dɛsk/	書桌 /书桌, 寫字檯 /寫字枱 /寫字臺 /寫字台
connect 动词	/kəˈnɛkt/	連接 /连接
purple 名词	/ˈpɝ.pl̩/	紫色
slide 名词	/ˈslaɪ̯d/	滑梯
segment 名词	/ˈsɛɡmɛnt/	片斷 /片断, 段
laser 名词	/ˈleɪzɚ/	激光, 雷射

视频中出现的短语动词

单词	发音	释义
hand over 动词		交出, 移交
zoom out 动词	/ˌzuːm ˈaʊt/	縮小 /缩小
end up 动词		到达

值得反复跟读的句子

视频中简短而完整的句子，可以直接用在日常对话里：

Let me enable it and let's see what happens.
Let's place the breadboard back on the table.
All right, let's move on to the next section.
I've changed to least squares again.

视频中的语法

说话人最常用的结构，并附上视频中的原话：

结构	视频中的用法
条件句 if + 从句，will/would + 动词 — 条件及其结果	if I point at myself you'll even
现在完成时 have/has + 过去分词 — 过去发生但与现在仍有关联的事	I've connected · I've zoomed · I've changed
被动语态 be + 过去分词 — 强调发生了什么，而不是谁做的	is connected · are projected · is calculated

需要注意的发音

说话人用了 21 次缩略和弱读形式，例如 I'm, I've, we've。请按听到的简短形式来说。

“th” 音: smooth /smuːð/, underneath /ˌʌndɚˈniθ/, algorithm /ˈælɡəɹɪðm̩/
“sh” 和 “zh” 音: measurement /ˈmɛʒ.ə.mənt/, essentially /ɪˈsɛnʃəli/, orientation /ˌɔː.ɹi.ənˈteɪ.ʃən/, capture /ˈkæp.(t)ʃɚ/, translation /tɹænzˈleɪʃən/
长单词——注意重音位置: essentially /ɪˈsɛnʃəli/, orientation /ˌɔː.ɹi.ənˈteɪ.ʃən/, representation /ˌɹɛp.ɹə.zɛnˈteɪ.ʃən/, radiation /ˌɹeɪ.diˈeɪ.ʃən/, differently /ˈdɪf.ə.ɹənt.li/

中文母语者容易读错的音：

/v/ — 不要读成 /w/，上齿轻触下唇: viewer /ˈvju.ɚ/, behave /bɪˈheɪv/, interactive /ˌɪn.tɚˈæk.tɪv/, convey /kənˈveɪ/, whichever /wɪt͡ʃˈɛvɚ/
词尾辅音 — 要读清楚，后面不要加元音: measurement /ˈmɛʒ.ə.mənt/, rotate /ˈɹoʊ.teɪt/, bounce /baʊns/, desk /dɛsk/, connect /kəˈnɛkt/
/r/ 和 /l/ 的区别: reflect /ɹɪˈflɛkt/, translation /tɹænzˈleɪʃən/, roughly /ˈɹʌf.li/, differently /ˈdɪf.ə.ɹənt.li/, surprisingly /səˈpɹaɪzɪŋli/

如何用这段视频练习

先完整听一遍视频，不要开口，记下不认识的单词。
先用 0.75 倍速逐句跟读，熟练之后再回到正常速度。
录下自己的声音并与原声对比，特别注意 sensor, measurement, ceiling 这类单词。

什么是跟读法？

跟读法 (Shadowing) 是一种有科学依据的语言学习技巧，最初开发用于专业口译员的培训，并由多语言者Alexander Arguelles博士普及。这个方法简单而强大：您在听英语母语原声的同时立即大声重复——就像是一个延迟1-2秒紧跟说话者的影子。与被动听力或语法练习不同，跟读法强迫您的大脑和口腔肌肉同时处理并模仿真实的讲话模式。研究表明它能显着提高发音准确性，语调，节奏，连读，听力理解和口语流利度——使其成为雅思口语备考和真实英语交流最有效的方法之一。

影子跟读法: 阅读完整分步指南 →

跟读练习: Turn a Time-of-Flight Sensor into a 3D Scanner - 通过视频学习英语口语

选择练习模式

下载应用

推荐视频

Making a 360° Controller Smaller than a Penny!

FAST Scan (Focused Assessment with Sonography in Trauma) - STEP by STEP

We Traveled Back in Time. Now Physicists Are Angry.

The Most Overengineered Part of Your iPhone

12 Camera Movements for CINEMATIC FOOTAGE - CREATIVE SHOT IDEAS for BETTER B-ROLL - Video Shot Ideas

How to Absorb Books 3x Faster in 7 Days (from a Med Student)

3-D Brain Scans | Pathways 2

Give me 31 minutes, I will make PyTorch click forever

Types of Proximity Sensors! #automation #engineering #science #robotics #electronics

Introduction to Optoelectronics and Photonics

PCB 101 Academy - Learn how printed circuit boards are assembled

I Made an EMP Device... It Actually Works