Robotics — Öğrenme rehberi
Connect observations, state, maps and behavior through existing explanations and experiments.
Temel ilkelerden uygulamaya uzanan düzenli öğrenme yolları.
Before you start Start with sensor, position and velocity terminology. Frames use vectors and matrices; estimation uses probability. Each topic links to its background.
1. System and runtime
Trace sensing through commands, then inspect communication and logs.
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The autonomous robot system
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ROS 2 communication
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Recording and replaying logs
Background: ROS 2 communication
- Lab · Run in browserrosbag2 kullanarak robot arızalarını yeniden üretme: girdiler, TF ve süre
2. Working with frames
Express the same point in different frames.
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Coordinate transforms
- Primer · Run in browserRobot koordinat dönüşümleri: matrisler, kuaterniyonlar ve TF
3. Estimating motion and state
Distinguish integrated motion from corrections using multiple sensors.
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Wheel odometry
Background: Coordinate transforms
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IMU bias and noise
- Lab · Run in browserSabit bir IMU logunu okuyun: sapma, dağılım ve Allan sapması
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GNSS positioning and error
- Primer · Run in browserGNSS Nasıl Çalışır ve Başlıca Ürünleri — u-blox, Trimble, Fixposition
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Sensor fusion
Background: Frames and time · Kalman filtering
4. Registration and mapping
Explore registration failures before studying the full SLAM system.
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Point-cloud registration with ICP
Background: Coordinate transforms
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LiDAR SLAM
Background: Point-cloud registration with ICP · Pose-graph update
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Evaluating SLAM accuracy
Background: LiDAR SLAM
- Lab · Run in browserSLAM nasıl değerlendirilir — ATE, RPE, çalışma süresi ve hatalar
5. From maps to motion
Separate map representation, paths and behavior, then connect them with Nav2.
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Occupancy grids
Background: Coordinate transforms
More questions on this topic
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Path search on a map
Background: Occupancy grids
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Behavior selection
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