Visual SLAM Overview
Uses camera as core sensor to achieve SLAM without LiDAR.
Technical Process
- Sensor input (monocular, stereo, RGB-D)
- Frontend (visual odometry):
- Feature point method (ORB-SLAM)
- Direct method (LSD-SLAM, DSO)
- Backend optimization (g2o, GTSAM, EKF)
- Loop closure detection (DBoW2)
- Mapping (sparse/dense)
Classic Solutions
ORB-SLAM Series
- Feature point V-SLAM
- Supports monocular/stereo/RGB-D
- Centimeter-level positioning accuracy
RTAB-Map
- Real-time appearance-based mapping
- Innovative memory management (WM/STM/LTM architecture)
VINS-Fusion
- Tightly coupled visual-inertial SLAM
- Sliding window optimization
- Loop closure detection
- Multi-sensor fusion
LSD-SLAM / DSO
- Direct method SLAM
OpenVSLAM / ORB-SLAM3
- Multi-map system
- Improved IMU fusion
Application Scenarios
- Indoor robots
- AR/VR
- UAVs
- Service robot navigation
- Autonomous driving visual positioning module
Technical Challenges
- Dynamic object interference
- Weak texture scenes
- Real-time performance vs accuracy balance