Computer Vision for Robotics

CV-Robo: Fusion of ML, optics, sensor fusion, and control theory enabling robots to perceive, interpret, and act in 3D dynamic envs. Core pipeline: img acq → preproc → feat extr → obj det/seg → 3D recon → pose est → nav/action. Sensors: RGB-D cams (Kinect, RealSense), stereo pairs, event cams (DAVIS), LiDAR-fused vis. Preproc: noise red (BM3D, NLM), illum norm (CLAHE), dist corr (cam calib via Zhang’s meth), dyn range adj. Feat extr: SIFT, SURF, ORB (scale/rot inv), LBP (texture), HOG (shape). Deep fe: CNN-based (ResNet, EfficientNet) via transfer learn or end-to-end train on Robo datasets (YCB-Video, BOP). Obj det: 2D → YOLOv8, Faster R-CNN, DETR; 3D → PVN3D, PointFusion, SO-Net. Seg: Mask R-CNN, DeepLabv3+, SAM (zero-shot cap). 3D recon: SFM (sparse), MVS (dense), TSDF fusing depth imgs (KinectFusion, ElasticFusion). SLAM integration: VI-SLAM (MSCKF, OKVIS), VIO (tight/loose coup), neural SLAM (iMAP, NICE-SLAM). Pose est: 6D obj pose via PVN3D (KP + voting), DenseFusion (RGB-D feat fusion), EPOS (corr-based). Template match: MODIFIED-IC-GN for planar/rigid obj track. Nav & manip: vis-guided nav (VGGS, CAD-RNN), affordance det (WHERE2ACT), visual servoing (IBVS, PBVS) using Jacobian-based ctrl laws. Sensor fusion: Kalman filters (EKF, UKF), factor graphs (GTSAM), deep fusion nets (Early/Mid/Late fusion). Real-time const: model comp (pruning, quantization), edge infer (TensorRT, OpenVINO), async proc (ROS2 nodes, DDS). Domain gaps: sim-to-real via domain rand (NVIDIA Isaac, AirSim), CAD aug, adversarial DA (CycleGAN). Failure modes: occlusion (part-based mod), clutter (context-aware det), dyn back (background sub, MOG2), low-light (thermal fusion, denoising nets). Edge cases: specular surf (polarization cams), transp/reflect (multi-view polarimetry). SOTA: CLIP-guided det, diffusion-based recon, neuromorphic event proc. Challenges: comp eff (latency <50ms), robustness (adversarial perturb), gen (few-shot adapt), safety (uncalib risk). Pitfalls: overfit to lab envs, poor cam-lidar sync, uncalib extrinsics, ignoring motion blur, inadequate train diversity. Tools: OpenCV, PCL, ROS/ROS2, PyTorch3D, Detectron2, MMDetection3D. Eval metr: mAP, IoU, ADD(-S), VSD, AUC (pose err), SPL (nav). Best pracs: calib rigor (chessboard + bundle adj), annotate at scale (SAM + manual verif), version data (DVC), test in var sim/envs. Future: embodied vis (active percep), world models (TrajectoryNet), self-sup learning (DINOv2 feats), neurosymbolic fusion. Key apps: AMR nav (warehouse bots), surgical robo (da Vinci vis track), agri robo (crop phenotyping), drone insp (bridge crack det), space rovers (auton samp ret).