SLAM

In robotic mapping, simultaneous localization and mapping (SLAM) is the computational problem of constructing or updating a map of an unknown environment while simultaneously keeping track of an agent’s location within it.

2020

• [SLAM] AWESOME VO and VIO 07/27
• [SLAM] Opencv Camera model 정리 06/19
• [SLAM] Camera Models and distortion (Perspective, Fisheye, Omni) 06/15
• [SLAM] Bundle Adjustment의 Jacobian 계산 03/01
• [SLAM] IMU Filter (AHRS) 01/10

2019

• [SLAM] KAIST Urban dataset의 stereo image를 이용한 Visual SLAM 비교 실험 10/22

2017

• [SLAM] Robust Graph SLAM 03/04
• [SLAM] Graph-based SLAM with Landmark 02/26
• [SLAM] Graph-based SLAM (Pose graph SLAM) 02/26
• [SLAM] Least Squares (최소자승법) 02/26
• [SLAM] Particle Filter and Monte Carlo Localization 02/22
• [SLAM] Occupancy Grid Maps 02/21
• [SLAM] Sparse Extended Information Filter(SEIF) SLAM 계산 02/20
• [SLAM] Sparse Extended Information Filter(SEIF) SLAM 02/19
• [SLAM] Extended Information Filter(EIF) SLAM 02/19
• [SLAM] Unscented Kalman Filter(UKF) 02/17
• [SLAM] Extended Kalman Filter(EKF) SLAM 02/16
• [SLAM] Extended Kalman Filter(EKF) 예제 02/15
• [SLAM] Kalman filter and EKF(Extended Kalman Filter) 02/14
• [SLAM] Motion & Observation model 02/14
• [SLAM] Bayes filter(베이즈 필터) 02/13