In order to improve the outdoor long-endurance positioning accuracy of robots,the developing and application of graph optimization-based global navigation satellite system(GNSS)/stereo visual/inertial simultaneous localization and mapping(SLAM)system is proposed.As the extra geometrical constraints,the spatial line features are integrated into the threads of the front-end feature extraction and back-end pose optimization to enhance the pose estimates.At the same time,the graph structure for joint optimization is constructed via factor graphs and the global observation error model is further derived.The nearly 200-meter-long BullDog-CX robot substation experiment shows that compared with VINS-Fusion and PL-VINS,the proposed algorithm has achieved about 12.6%and 3.4%improvement in positioning accuracy,which provides a feasible scheme for long-endurance navigation of outdoor robots.
GNSS/stereo visual/inertial SLAM systemgraph optimizationline feature constraintglobal observationmulti-sensor fusion
万方数据
维普
