SLAM问题本质上是状态估计问题，利用传感器信息同时对机器人位姿和环境

The SLAM problem is essentially a state estimation problem, which uses sensor information to simultaneously estimate the state of the robot's pose and environment. As shown in Figure 13, the SLAM method based on graph optimization divides the SLAM problem into two parts, the front end and the back end. The front end is responsible for constructing the pose image, and the back end adjusts the pose through optimization means to optimize the configuration of the image.

The SLAM problem is essentially a state estimation problem, using sensor information to estimate the robot's position and environment at the same time. As shown in Figure 13, the SLAM method based on graph optimization divides the SLAM problem into two parts, the front end is responsible for building the bit position, while the back end adjusts the position by means of optimization to optimize the configuration of the graph.

Slam is essentially a state estimation problem, which uses sensor information to estimate the robot's pose and environment. As shown in Figure 13, slam method based on graph optimization divides SLAM problem into two parts: front-end and back-end. The front-end is responsible for the construction of pose map, and the rear end adjusts the configuration of the graph through optimization.<br>