对于视觉惯性里程计(VIO), 视觉遮挡、运动物体等复杂场景可能带来异常的视觉测量, 导致系统定位精确度急剧下降。对此, 提出了一种新的 VIO异常视觉测量的检测和处理方法。通过选取检测指标、设置先验阈值和设计检测分类器, 实现对异常视觉测量的检测与分类；提出多传感器融合策略和自适应误差加权算法, 及时消除与实际运动不一致的异常视觉测量的影响；最后, 将异常视觉测量检测和处理算法整合到基于关键帧的视觉惯性里程计(OKVIS)系统中, 提出了视觉惯性里程计的异常检测和处理(EDS-VIO)系统框架。在复杂场景仿真数据集上的评测结果表明, EDS-VIO比 OKVIS取得了更好的性能, 定位误差均值从 1.045 m下降到 0.437 m。所提方法较好地提升了 VIO在复杂场景中的定位精确度和鲁棒性。

For Visual-Inertial Odometry(VIO), complex scenes, such as visual occlusion and moving objects may bring about abnormal visual measurements, which leads to dramatically drop of the positioning accuracy. To this end, a new method is proposed for detecting and processing abnormal visual measurements in VIO. Firstly, by selecting the detection index, setting the prior threshold and designing the detection classifier, the classification and detection of abnormal visual measurement are realized. After that, a multi-sensor fusion strategy and an adaptive error weighting algorithm are proposed, and these algorithms timely eliminate the influence of abnormal visual measurement which is inconsistent with the actual motion. Finally, the detection and processing algorithm of abnormal visual measurement are integrated into Open Keyframe-based Visual-Inertial SLAM(OKVIS), and the Error Detection and Solution of Visual-Inertial Odometry(EDS-VIO) framework is proposed. Evaluation results on the complex scene simulated dataset show that, compared with OKVIS, EDS-VIO has achieved better performance on the dataset, the average location error has been reduced from 1.045 m to 0.437 m. EDS-VIO improves the positioning accuracy and robustness of the VIO in complex scenes.