Introduction

Computer vision is the study of visual data from a disembodied perspective.

Computer vision spans a broad range of tasks, including object detection, semantic and instance segmentation, depth estimation, pose estimation, feature matching, and multi-object tracking. In 3D vision, common areas include point-cloud processing, point-cloud registration, multi-view geometry, camera modeling, photogrammetry, stereo vision, and 3D reconstruction.

For robotics and autonomous systems, these capabilities are integrated into a larger autonomy stack covering perception, planning, and control. Related system-level work includes visual odometry, SLAM, extrinsic calibration, multi-sensor fusion, and precise time synchronization acros s cameras, LiDAR, radar, IMUs, and other sensors.

Production perception systems also require efficient camera and sensor pipelines optimized for low latency, high throughput, accurate calibration, and reliable real-time operation.

Future Work:

• SLAM(Simultaneous Localization and Mapping)