• Sensor_Fusion
  • 센서 퓨전
  • README
  • TO-READ
  • Mind_map
  • Paper (미분류)
    • FusionNet2016(100%)
    • DeepSlideingShape2016(70%)
    • FuseNe2016(50%)
    • HHA-Fusion2016(30%)
    • Decision-Level-Fusion2017(50%)
    • VANET_3D2017(50%)
    • Lidar_Vision2014
    • TutleBot2017
    • MV3D2017(70%)
    • Deeply-Fused-Net2016
    • Asynchronous_Data_fusion
  • Implementation (미분류)
    • MV3D
  • Post (미분류)
    • cs231n_Dempster-Shafer
    • Sensor-Modality-Fusion2017
  • Pose Estimation
    • 3D Pose Estimation
    • POSIT-Algorithm
    • Convoys-pose-estimation-2017
  • edx
  • EDX-Introductioon
  • EDX-Chapter1
    • README
    • 1-1-Course Introduction (100%)
    • 1-4-Primer on Statistics (20%)
    • 1-references-matlab
    • 1-assigment
  • EDX-Chapter2
    • 2-1-An Introduction Bayesian statistics
    • 2-2-Bayes rule
    • 2-3-Building Blocks of bayesian models
    • 2-4-Bayesian decision theory
    • 2-5-Cos function in Bayesian Decision Theory
  • EDX-Chapter3
    • 3-1-Filtering, smoothing and prediction
    • 3-2-State Space Models
    • 3-3-Conditional independencies in state space models
    • 3-4-Optimal filtering
  • EDX-Chapter4
  • EDX-Chapter5
  • EDX-Chapter6
  • EDX-Chapter7
  • Devices
  • Radar
  • Lidar
  • convert_coord
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Paper (미분류)

Real Time Lidar and Radar High-Level Fusion for Obstacle Detection and Tracking with evaluation on a ground truth: 2018

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