Excavator’s main tasks include digging, trenching, and ground leveling tasks at construction sites, and work efficiency and safety can be improved by using an autonomous excavator. A prerequisite step to achieving an autonomous excavation is to obtain a sound perception of the surrounding ground. For this, a LiDAR sensor has been widely used to scan the environment. However, the point cloud generated by the LiDAR is not ideal for surface reconstruction to generate a ground map, as it suffers from flaws such as noise and outlier points. Thus, a series of enhancements must be done before fitting a surface on the point cloud data.
To tackle this issue, our paper proposes advanced methodologies to improve the surface reconstruction for the group map generation, which are applied to the raw data obtained from the lidar sensor before applying the surface reconstruction algorithms.
As the first step, a sensor configured to report the furthest reflection of the fired laser helps reduce noisy data. The next step is to remove outliers from a data set. Further, a region of interest around the digging area was defined to exclude any unnecessary environment and objects, which can significantly reduce the computation time. Finally, the data points enhanced (i.e., more suitable for surface reconstruction algorithm) through the previous steps were used to fit or approximate the ground surface. The advanced front surface reconstruction method was applied to generate the surface because it does not require the normal vectors of each point on top of being robust to noisy data.
Implementing the proposed surface reconstruction methods in the excavation application will allow for better identification of the ground shape and provide a solid foundation for the generation of optimal trajectory, accurate tracking control, and safety evaluation that are required for completing a successful autonomous excavation.
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