Expert-level computer vision for robotics covering camera models, feature detection, object detection, pose estimation, visual SLAM, and deep learning for perception.
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Pinhole camera: projects 3D point to 2D image plane through focal point. Intrinsic matrix K: focal lengths and principal point, 5 parameters. Distortion: radial and tangential lens distortion, correct before processing. Stereo camera: two calibrated cameras, disparity gives depth. Depth cameras: structured light or time-of-flight, direct depth measurement.
Corners: Harris detector, stable under rotation, used for tracking. SIFT: scale and rotation invariant features, robust but slow. ORB: binary features, fast, used in real-time SLAM. Feature matching: descriptor distance, ratio test, RANSAC for outlier rejection.
Classical: HOG plus SVM, sliding window, deformable parts model. YOLO: single-shot detector, real-time, divides image into grid. Faster R-CNN: region proposal network, accurate but slower. Segmentation: semantic labels per pixel, instance segmentation separates objects.
SLAM: simultaneous localization and mapping, build map while tracking position. Visual odometry: estimate motion from consecutive image frames. Loop closure: detect revisited places, correct accumulated drift. ORB-SLAM3: feature-based, monocular and RGB-D, open-source reference system. NERF and 3DGS: neural radiance fields for photorealistic scene reconstruction.
| Pitfall | Fix |
|---|---|
| Skipping camera calibration | Always calibrate, reprojection error should be below 1 pixel |
| Ignoring lighting variation | Test and train under diverse lighting conditions |
| Assuming static scene in SLAM | Handle dynamic objects explicitly |
| Overconfident pose estimates | Always provide uncertainty with pose estimates |
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概要と使いどころ
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日本語の概要は準備中です。原文の説明を表示しています。
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日本語の概要は準備中です。原文の説明を表示しています。
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日本語の概要は準備中です。原文の説明を表示しています。
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日本語の概要は準備中です。原文の説明を表示しています。
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日本語の概要は準備中です。原文の説明を表示しています。
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日本語の概要は準備中です。原文の説明を表示しています。