A Multi-sensor Fusion Algorithm Based on View-cone Distance and Adaptive Weighted Kalman Filter
To improve the environmental perception ability of intelligent driving systems,multimodal sensors have been integrated with the artificial intelligence technology to solve the problem of single-modal sensors,such as poor recognition and vulnerability to interference in environmental perception.However,problems of feature matching between cross-modal sensors,such as inconsistent feature representations,sensing errors,and delay errors,persist.To address these problems,this study proposes a view-cone distance metric-based method,constructs a target-matching matrix,and uses the Hungarian algorithm for inter-frame association matching.Based on the intersection over union(IOU)and Euclidean distance metrics,the multiple object tracking accuracy(MOTA)of the proposed method improved to 81.22％and 58.62％in the cyclist and pedestrian categories,respectively.An adaptive weight-adjustment technique was also introduced to optimize the Kalman filter algorithm so that low-complexity and efficient cross-modal sensor fusion target detection and tracking could be achieved.Compared with the individual camera and light detection and ranging(LIDAR)predictions,the root mean square error of the fusion method reaches 0.3490,denoting a reduction by 30.37％and 30.53％compared with the camera and LIDAR methods,respectively,and confirming the accuracy of the proposed adaptive weighting Kalman filter fusion tracking method.The multi-target tracking experimental test conducted on the KITTI dataset achieved an accuracy of 88.25％,comparable to the performance of current mainstream methods.The test results under multiple weather conditions also demonstrated excellent performance,with target detection accuracies of 96.40％,75.51％,and 91.87％for vehicles,pedestrians,and cyclists,respectively.Compared to a single sensor,the fusion method attained superior detection results under multiple road conditions,improved the reliability and robustness of the system,and laid a solid foundation for the further development of driverless technology.
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