Abstract: A multi-sensor fusion SLAM system and a robot. The system operates on a mobile robot and comprises: a visual inertia module, a laser scanning matching module, a loop closure detection module, and a visual laser image optimization module. According to the multi-sensor fusion SLAM system and the robot, the calculation amount of laser matching constraint optimization can be reduced by using a voxel subgraph so that the pose calculation is more accurate, accumulated errors of long-time operation of the system can be corrected in time by means of sufficient fusion of modules, and the robustness of the system and the accuracy of positioning and mapping are integrally improved.

Abstract: A slope location correction method, comprising the following steps: measuring location data, in a predetermined area, of a robot by using an inertial measurement element of the robot, and calculating a pose of the robot in the predetermined area according to the location data: determining whether there is a slope in the predetermined area by means of the pose of the robot; and if it is determined that there is a slope in the predetermined area, compensating for a location error at the slope by using the pose corresponding to the slope. The present application further relates to a slope location correction apparatus, a robot and a readable storage medium.

Abstract: A fusion positioning method, comprising: sampling an environmental object by means of a two-dimensional laser radar carried by a robot, so as to acquire radar point cloud-related data of the environmental object; performing adjacent matching on the position Pi of a landmark Li in the environmental object on a road sign map and the landmark map, so as to acquire the position Pj of a landmark Lj nearest to the position Pi on the landmark map; performing scan matching on a two-dimensional grid map by using point cloud data, from which distortion is removed, of the environmental object, so as to acquire a global pose of the robot in the two-dimensional grid map, and taking same as a global pose observation value G pose of the robot; and according the position Pi of the landmark Li on the landmark map, the position Pj of the landmark Lj on the landmark map, and an estimated pose and the global pose observation value G pose of the robot, performing an iterative optimization on the pose of the robot until the error

Abstract: A multi-sensor fusion SLAM system and a robot. The system operates on a mobile robot and comprises: a visual inertia module, a laser scanning matching module, a loop closure detection module, and a visual laser image optimization module. According to the multi-sensor fusion SLAM system and the robot, the calculation amount of laser matching constraint optimization can be reduced by using a voxel subgraph so that the pose calculation is more accurate, accumulated errors of long-time operation of the system can be corrected in time by means of sufficient fusion of modules, and the robustness of the system and the accuracy of positioning and mapping are integrally improved.

Abstract: The present invention provides a loop closure detection method and system, a multi-sensor fusion SLAM system, a robot, and a medium. Said system runs on a mobile robot, and comprises a similarity detection unit, a visual pose solving unit, and a laser pose solving unit. According to the loop closure detection system, the multi-sensor fusion SLAM system and the robot provided in the present invention, the speed and accuracy of loop closure detection in cases of a change in a viewing angle of the robot, a change in the environmental brightness, a weak texture, etc. can be significantly improved.