The dusty environments of autonomous excavation can affect the performance of the sensors onboard the vehicle. Specifically, airborne dust clouds can be perceived as solid objects if not addressed appropriately, which can lead to irrational movements that risk safety. In this article, a light detection and ranging (LiDAR) and red-green-blue (RGB) image sensor fusion model was developed to filter airborne dust particles. The proposed approach processes the RGB and LiDAR data in separate convolutional neural network (CNN) models and combines the predictions in a late fusion model for enhanced real-time performance. Testing shows that the proposed fusion model has an F1 score at least 2.64% higher than a LiDAR only CNN model and a dynamic radius outlier removal paired with low-intensity outlier removal (LIOR-DROR) when dust clouds are around 3 m from the sensors.

中文翻译：

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RGB-LiDAR 传感器融合，用于自主挖掘应用中的灰尘去过滤


自主挖掘的多尘环境会影响车辆上传感器的性能。具体来说，如果处理不当，空气中的尘埃云可能会被视为固体物体，这可能导致危及安全的非理性运动。本文开发了一种光探测与测距 （LiDAR） 和红-绿-蓝 （RGB） 图像传感器融合模型来过滤空气中的尘埃颗粒。所提出的方法在单独的卷积神经网络 （CNN） 模型中处理 RGB 和 LiDAR 数据，并将预测结果组合在后期融合模型中以增强实时性能。测试表明，当尘埃云距离传感器约 3 m 时，所提出的融合模型的 F1 分数至少比仅使用 LiDAR 的 CNN 模型高 2.64%，并且动态半径异常值去除与低强度异常值去除 （LIOR-DROR） 配对。