Robotics — 학습 가이드
Connect observations, state, maps and behavior through existing explanations and experiments.
기초 원리부터 실제 응용까지 체계적인 학습 경로를 따라갑니다.
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 browserrosbag2를 사용하여 로봇 오류를 재현합니다: 입력, TF 및 시간
2. Working with frames
Express the same point in different frames.
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Coordinate transforms
- Primer · Run in browser로봇 좌표 변환: 행렬, 쿼터니언 및 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 browser정지형 IMU 로그 판독: 편향, 산포 및 알란 편차
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GNSS positioning and error
- Primer · Run in browserGNSS 작동 방식 및 주요 제품 — u-blox, Trimble, Fixposition
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Sensor fusion
Background: Frames and time · Kalman filtering
- Primer센서 융합 입문 — 여러 센서를 활용하여 하나의 세계 모델 구축하기
- Primer · Run in browser센서 융합이 실패하는 이유 — 타이밍, 프레임 및 외부 보정
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
- Lab · Run in browserICP가 실패하는 이유: 초기화, 이상치 및 대칭 기하학적 구조
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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 평가 방법 — ATE, RPE, 런타임 및 실패율
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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