Shadowing Practice: Turn a Time-of-Flight Sensor into a 3D Scanner - Learn English Speaking with Video
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Today I want to show you a little setup that I've got here on the breadboard.
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These are all parts from Aliexpress so it's a relatively cheap setup.
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The ESP cost me about £4, the time of flight sensor cost me about £5 and this IMU was about £15.
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So it's the most expensive part of the system and if we wanted we could replace it with a cheaper alternative.
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So this whole setup cost about £24, that would be $30 or €28.
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Eros.
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So the reason why this tiny LiDAR is so special is that it is a multi -zone time -of -flight sensor.
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So what that means is that instead of shooting out a single ray
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and measuring the distance at which that ray bounces off an obstacle, it shoots out a 8x8 grid.
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So those are 64 rays coming out of it that will bounce back once they hit an obstacle.
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Now I've connected the breadboard to my computer via a USB cable and the LED is on so we know it's powered.
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Here in the viewer now we can see that we've got roughly the same layout for the two breakout boards.
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We have the IMU in purple and the time -of -flight sensor in green.
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Here on the right we have the status panel showing the live data
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and we can see that the IMU is connected
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and we're getting measurement data from the time -of -flight at about 15 Hz.
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Now you see these blue rays shooting out from the time -of -flight sensor.
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There are 64 of them arranged in an 8x8 grid and each represents one zone that the sensor can measure.
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Notice how they don't start right at the sensor, there's a small gap.
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And they extend outward, but not infinitely.
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If I zoom out, you can see that.
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These line segments span the sensor's measurable range from 2 cm to 4 m.
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Now, this is fundamentally different from how cameras work.
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A camera captures light across a continuous image, but the sensor, it can only measure along these specific rays.
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The points you see here, they're locked to these line segments.
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They cannot appear anywhere else
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and you can see as I slide my hand across the sensor you see the points going left
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and right and also back and forth
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and I can also hold my hand like this
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and just slide my finger underneath it
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and you can see there's a wave changing the points from the from the palm of my hand
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now here's where it gets interesting let me zoom out
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and we will see the points here that are projected onto the ceiling of the room where I'm at.
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And you see in the settings panel this option here for clipping to measurement.
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Let me enable it and let's see what happens.
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Do you see how the rays now stop at the measure points instead of extending to the maximum range?
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This is a much more accurate representation of what's actually physically happening.
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The sensor shoots out infrared laser pulses.
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They travel along these paths, bounce off of whatever obstacle they hit, and reflect back.
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The sensor measures how long that took, that's the time of flight, and calculates the distance.
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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.
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And that's exactly what's happening in the real world.
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Some laser pulses are hitting my hand, others are missing it and hitting the ceiling.
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I think this visualization does a much better job of conveying what this sensor does compared to some 2D heatmaps
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that you typically see online.
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And it's a lot of fun to play with.
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Alright, I've zoomed back in because I want to talk a bit about this purple breakout board here.
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It's an IMU.
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And watch what happens in a visualizer when I rotate the breadboard in the real world.
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It also rotates.
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The IMU on the breadboard is tracking the orientation in real time
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and since both the IMU
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and the time -of -flight sensor are stuck to the same breadboard we always know which direction the sensor is pointing.
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Let me zoom back out and you can kind of see how if I point around I
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can measure different points around my room.
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Let's place the breadboard back on the table you may notice
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that the points are a bit jittery
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and that's just sensor noise from radiation coming from the window or noise from the actual sensor measurement.
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Now if I come over here to the side panel
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and enable that filter you can see how the noise kind of smooths out
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and I can control the strength of the filter with this slider.
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What's happening under the hood is an exponential moving average.
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At 0 .5 strength, each new position is calculated as 50 % of the new measurement plus 50 % of the previous position.
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So it's essentially averaging the current and previous readings.
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If I crank up the strength to 0 .9, it gets even smoother.
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But now there's a noticeable lag.
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So when I move this two -point from the ceiling to the wall, you can see that the points are really smooth,
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but there's a noticeable delay.
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At 90 % previous and only 10 % new measurement, the visualization can't keep up with the fast movements.
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So it's a trade -off between smoothness and responsiveness.
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If I move the strength all the way down to 0, that essentially means there is no filter.
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It's the same as if this checkbox was left unchecked.
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And watch what happens if I move all the way to 1.
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That means that the previous measurement gets all the weight and 0 % is assigned to the new measurement.
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So even though I'm rotating the breadboard around the room, you see that no new measurements are coming in.
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Let me move the slider back to 0 .5, which I found to be a good strength for the filter.
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Alright, let's move on to the next section.
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The viewer can also fit the plane to the measured points.
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When I enable plane fitting with the least squares method and point the sensor at a flat surface like the ceiling,
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you can see if it's a nice plane.
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But watch what happens when I point the sensor at both the wall and the ceiling at the same time.
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The fitted plane ends up diagonally, trying to minimize the error across all points, and this is not very useful.
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This is where RANSAC comes in.
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RANSAC stands for Random Sample Consensus.
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When I switch to this method, watch how it behaves differently.
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RANSAC works by randomly picking three of these sampled points, the minimum to define a plane.
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Fitting a plane to those three, then checking how many other points lie close to that plane,
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and it repeats this about a hundred times and keeps the best fit.
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The key difference is, instead of trying to please all the points equally,
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Ransack finds the plane that the majority of points agree on and ignores the outliers.
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So now when I point again at both the ceiling and the wall, it snaps to whichever surface has more points.
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Rather than compromising with a meaningless diagonal.
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And so even if I come in with a pen to disturb the sensor measurements,
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you can see how it still is able to be robust to that and fit the ceiling nicely.
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And if I change to least squares,
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you see how much the disturbance from waving this pen on top of the sensor affects the fitted plane.
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I'm going to change again to RANSACK now.
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If you cover enough points you can see that it still affects the plane fitting algorithm,
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but most of the time it behaves much better than least squares.
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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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Bu dersin kelimeleri ve konuşma notları
Bu konuşma dersi “Turn a Time-of-Flight Sensor into a 3D Scanner” videosuna dayanıyor. En çok tekrarlanan kelimeler: sensor, Imu, measurement, plane, breadboard. Bu videoda gölgeleme çalışması için 42 cümle ve 1785 kelime var. Konuşma bölümü 11:19 sürüyor. Konuşmacı dakikada yaklaşık 158 kelimeyle, günlük konuşmaya yakın doğal bir hızda konuşuyor. Kelimelerin yalnızca %78’i İngilizcede en sık kullanılan 3.000 kelime arasında, bu yüzden kelime dağarcığı zorlayıcı.
Bu videodaki önemli kelimeler
Videodan öğrenmeye değer 15 kelime, telaffuzu ve anlamıyla:
| Kelime | Telaffuz | Anlam |
|---|---|---|
| sensor isim | /ˈsɛn.sɚ/ | duyarga, sensör |
| measurement isim | /ˈmɛʒ.ə.mənt/ | ölçüm |
| ceiling isim | /ˈsilɪŋ/ | tavan |
| filter isim | /ˈfɪltɚ/ | filtre |
| bounce fiil | /baʊns/ | sekmek |
| viewer isim | /ˈvju.ɚ/ | izleyici, seyirci |
| obstacle isim | /ˈɑbstəkəl/ | engel, ket |
| desk isim | /dɛsk/ | masa, sıra |
| extend fiil | /ɛkˈstɛnd/ | genişletmek |
| purple isim | /ˈpɝ.pl̩/ | mor, erguvani |
| slide isim | /ˈslaɪ̯d/ | kaydırak |
| laser isim | /ˈleɪzɚ/ | lazer |
| grid isim | /ɡɹɪd/ | şebeke |
| pulse isim | /pʌls/ | nabız |
| behave fiil | /bɪˈheɪv/ | davranmak |
Tekrar etmeye değer cümleler
Videodan, günlük konuşmada yeniden kullanabileceğiniz kısa ve tam cümleler:
- 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.
Bu videodaki dil bilgisi
Konuşmacının en çok kullandığı yapılar, videodaki sözcüklerin aynısıyla:
| Yapı | Videoda |
|---|---|
| Koşul cümleleri if + cümle, will/would + fiil — bir koşul ve sonucu | if I point at myself you'll even |
| Present perfect have/has + fiilin üçüncü hâli — geçmişte olan ama şimdi de önemli olan bir eylem | I've connected · I've zoomed · I've changed |
| Edilgen yapı be + fiilin üçüncü hâli — kimin yaptığı değil, ne olduğu önemli | is connected · are projected · is calculated |
Dikkat edilecek telaffuzlar
Konuşmacı I'm, I've, we've gibi kısaltılmış biçimleri 21 kez kullanıyor. Bunları duyduğunuz gibi kısa söyleyin.
- “th” sesleri: smooth /smuːð/, underneath /ˌʌndɚˈniθ/, algorithm /ˈælɡəɹɪðm̩/
- “sh” ve “zh” sesleri: measurement /ˈmɛʒ.ə.mənt/, essentially /ɪˈsɛnʃəli/, orientation /ˌɔː.ɹi.ənˈteɪ.ʃən/, capture /ˈkæp.(t)ʃɚ/, translation /tɹænzˈleɪʃən/
- Uzun kelimeler — vurguyu doğru yere koyun: essentially /ɪˈsɛnʃəli/, orientation /ˌɔː.ɹi.ənˈteɪ.ʃən/, representation /ˌɹɛp.ɹə.zɛnˈteɪ.ʃən/, radiation /ˌɹeɪ.diˈeɪ.ʃən/, differently /ˈdɪf.ə.ɹənt.li/
Türkçe konuşanların zorlandığı sesler:
- /w/ — dudaklar yuvarlak, /v/ değil: equally /ˈiːkwəli/, whichever /wɪt͡ʃˈɛvɚ/, outward /ˈaʊt.wɚd/
- Kelime başındaki ünsüz kümesi — araya ünlü eklemeyin: smooth /smuːð/, viewer /ˈvju.ɚ/, slide /ˈslaɪ̯d/, grid /ɡɹɪd/, cloud /ˈklaʊ̯d/
- /æ/ — “e”den daha açık: calculate /ˈkælkjʊleɪt/, capture /ˈkæp.(t)ʃɚ/, translation /tɹænzˈleɪʃən/, snap /snæp/, interactive /ˌɪn.tɚˈæk.tɪv/
Bu videoyla nasıl çalışılır
- Videonun tamamını konuşmadan bir kez dinleyin ve bilmediğiniz kelimeleri not edin.
- 0,75× hızla başlayın, cümle cümle tekrar edin ve kolaylaşınca normal hıza dönün.
- Kendinizi kaydedin ve orijinaliyle karşılaştırın; sensor, measurement, ceiling gibi kelimelere özellikle dikkat edin.
Gölgeleme Tekniği Nedir?
Gölgeleme, başlangıçta profesyonel tercüman eğitimi için geliştirilen ve çok dilli Dr. Alexander Arguelles tarafından popüler hale getirilen, bilim destekli bir dil öğrenme tekniğidir. Yöntem basit ama güçlüdür: ana dili İngilizce olan bir sesi dinler ve hemen yüksek sesle tekrar edersiniz — konuşmacıyı 1-2 saniye gecikmeyle takip eden bir gölge gibi. Pasif dinleme veya dilbilgisi alıştırmalarının aksine, gölgeleme beyninizi ve ağız kaslarınızı gerçek konuşma kalıplarını eşzamanlı olarak işlemeye ve yeniden üretmeye zorlar. Araştırmalar, telaffuz doğruluğu, tonlama, ritim, bağlı konuşma, dinleme anlama ve konuşma akıcılığını önemli ölçüde geliştirdiğini göstermektedir — bu da onu IELTS Konuşma hazırlığı ve gerçek dünya İngilizce iletişimi için en etkili yöntemlerden biri yapar.