쉐도잉 연습: Turn a Time-of-Flight Sensor into a 3D Scanner - 영상으로 영어 말하기 배우기

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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.

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

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It's a really big difference.

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

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

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

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Mapping mode changes that.

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Instead of replacing points, it accumulates them.

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

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

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

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

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Here's where the magic really happens.

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You remember we have the IMU tracking orientation right?

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So watch what happens when I rotate the breadboard.

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Look at that!

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

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if I go under the desk I can see the floor

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

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and I can even put my left arm up

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and then my right arm up and

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because we're no longer just showing the instantaneous points

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

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

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

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and they all accumulate into a coherent point cloud.

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This is really cool!

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

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Now there's one limitation worth mentioning.

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

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

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

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

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And that's it!

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This concludes the video.

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

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and an IMU can be.

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

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and could be useful for a robotics project.

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

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이 레슨의 어휘와 말하기 포인트

이 말하기 레슨은 영상 “Turn a Time-of-Flight Sensor into a 3D Scanner”을(를) 바탕으로 합니다. 가장 자주 반복되는 단어는 다음과 같습니다: sensor, Imu, measurement, plane, breadboard. 이 영상에는 섀도잉할 문장 42개와 단어 1785개가 있습니다. 말하는 구간의 길이는 11:19입니다. 화자는 분당 약 158단어로, 일상 대화에 가까운 자연스러운 속도로 말합니다. 영어에서 가장 많이 쓰이는 3,000단어에 속하는 단어가 78%뿐이라 어휘가 어려운 편입니다.

이 영상의 핵심 어휘

영상에 나오는 익혀 둘 만한 단어 15개를 발음, 뜻과 함께 정리했습니다.

단어	발음	뜻
sensor 명사	/ˈsɛn.sɚ/	센서
measurement 명사	/ˈmɛʒ.ə.mənt/	측정
ceiling 명사	/ˈsilɪŋ/	천장
filter 명사	/ˈfɪltɚ/	여과기, 필터
viewer 명사	/ˈvju.ɚ/	관람자
obstacle 명사	/ˈɑbstəkəl/	장애, 장애물
desk 명사	/dɛsk/	책상
connect 동사	/kəˈnɛkt/	잇다, 연결하다
purple 명사	/ˈpɝ.pl̩/	자주색, 보라색
slide 명사	/ˈslaɪ̯d/	미끄럼틀, 미끄럼대
laser 명사	/ˈleɪzɚ/	레이저
grid 명사	/ɡɹɪd/	격자, 그리드
pulse 명사	/pʌls/	맥박
behave 동사	/bɪˈheɪv/	처신하다, 행동하다
calculate 동사	/ˈkælkjʊleɪt/	정산하다, 계산하다

영상에 나오는 구동사

단어	발음	뜻
zoom out 동사	/ˌzuːm ˈaʊt/	줌 아웃하다, 축소(縮小)하다

따라 말해 볼 만한 문장

영상에 나오는 짧고 완결된 문장으로, 일상 대화에서 그대로 쓸 수 있습니다:

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.

이 영상의 문법

화자가 가장 많이 쓰는 문형을 영상 속 실제 표현과 함께 정리했습니다.

문형	영상 속 표현
조건문 if + 절, will/would + 동사 — 조건과 그 결과	if I point at myself you'll even
현재완료 have/has + 과거분사 — 과거의 일이 지금도 관련이 있을 때	I've connected · I've zoomed · I've changed
수동태 be + 과거분사 — 누가 하는지보다 무슨 일이 일어나는지에 초점	is connected · are projected · is calculated

주의할 발음

화자는 I'm, I've, we've 같은 축약형과 약화된 형태를 21번 사용합니다. 들리는 대로 짧게 발음하세요.

“th” 소리: smooth /smuːð/, underneath /ˌʌndɚˈniθ/, algorithm /ˈælɡəɹɪðm̩/
“sh”와 “zh” 소리: measurement /ˈmɛʒ.ə.mənt/, essentially /ɪˈsɛnʃəli/, orientation /ˌɔː.ɹi.ənˈteɪ.ʃən/, capture /ˈkæp.(t)ʃɚ/, translation /tɹænzˈleɪʃən/
긴 단어 — 강세 위치에 주의: essentially /ɪˈsɛnʃəli/, orientation /ˌɔː.ɹi.ənˈteɪ.ʃən/, representation /ˌɹɛp.ɹə.zɛnˈteɪ.ʃən/, radiation /ˌɹeɪ.diˈeɪ.ʃən/, differently /ˈdɪf.ə.ɹənt.li/

한국어 화자가 어려워하는 소리:

/f/ — ㅍ(/p/)으로 바꾸지 말고 윗니를 아랫입술에 대기: filter /ˈfɪltɚ/, finger /ˈfɪŋɡɚ/, reflect /ɹɪˈflɛkt/, roughly /ˈɹʌf.li/, physically /ˈfɪz.ɪ.kli/
/v/ — ㅂ(/b/)과 구별하기: viewer /ˈvju.ɚ/, behave /bɪˈheɪv/, interactive /ˌɪn.tɚˈæk.tɪv/, convey /kənˈveɪ/, whichever /wɪt͡ʃˈɛvɚ/
/z/ — ㅈ이 아니라 성대를 울리는 /s/: zoom /zuːm/, laser /ˈleɪzɚ/, translation /tɹænzˈleɪʃən/, representation /ˌɹɛp.ɹə.zɛnˈteɪ.ʃən/, physically /ˈfɪz.ɪ.kli/

이 영상으로 연습하는 방법

먼저 말하지 않고 영상을 끝까지 듣고 모르는 단어를 적어 둡니다.
0.75배속으로 한 문장씩 섀도잉을 시작하고, 익숙해지면 보통 속도로 돌아갑니다.
자신의 목소리를 녹음해 원본과 비교하고, sensor, measurement, ceiling 같은 단어에 특히 주의합니다.

쉐도잉이란? 영어 실력을 빠르게 키우는 과학적 방법

쉐도잉(Shadowing)은 원래 전문 통역사 훈련을 위해 개발된 언어 학습 기법으로, 다언어 학자인 Dr. Alexander Arguelles에 의해 대중화된 방법입니다. 핵심 원리는 간단하지만 매우 강력합니다: 원어민의 영어를 들으면서 1~2초의 짧은 지연으로 즉시 소리 내어 따라 말하는 것——마치 '그림자(shadow)'처럼 화자를 따라가는 것입니다. 문법 공부나 수동적인 청취와 달리, 쉐도잉은 뇌와 입 근육이 동시에 실시간으로 영어를 처리하고 재현하도록 훈련합니다. 연구에 따르면 이 방법은 발음 정확도, 억양, 리듬, 연음, 청취력, 말하기 유창성을 크게 향상시킵니다. IELTS 스피킹 준비와 자연스러운 영어 소통을 원하는 분들에게 특히 효과적입니다.

섀도잉 방법: 단계별 전체 가이드 읽기 →

쉐도잉 연습: Turn a Time-of-Flight Sensor into a 3D Scanner - 영상으로 영어 말하기 배우기

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