Aiming at the problems of localization failure,mapping overlap drift and non-real-time operation of simultaneous localization and mapping(SLAM)in the degraded environment without global positioning system(GPS)signal and lack of environmental feature texture,a laser SLAM method based on spatial geometric features is proposed.At the front end of the algorithm proposed,a feature point extraction method based on the spatial geometric features of lines and surfaces is designed,and the residual function of point cloud registration is constructed according to the constraints of points,lines and surfaces,and the residual is optimized by Gauss-Newton method for point cloud registration.At the back end of the algorithm,subgraphs are constructed based on keyframes,and precise poses are obtained by map-to-map matching among subgraphs.Interpolation is used to fuse the poses of the front and back ends to achieve global positioning optimization.Extensive experiments conducted in both simulated and real-world degraded environment demonstrate that the pose estimation error of the proposed algorithm remains below 5%within a 20-meter odometer test.The mapping effect under the degraded environment is better than that of Hector,Gmapping and Cartographer algorithms,and the average speed about map updating has increased by nearly 4 times.
laser simultaneous localization and mappingfeature extractionenvironment perceptiondegraded environment
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