  Accurate depth-sensing plays a crucial role in securing a high success rate
of robotic harvesting in natural orchard environments. Solid-state LiDAR (SSL),
a recently introduced LiDAR technique, can perceive high-resolution geometric
information of the scenes, which can be potential utilised to receive accurate
depth information. Meanwhile, the fusion of the sensory information from LiDAR
and camera can significantly enhance the sensing ability of the harvesting
robots. This work introduces a LiDAR-camera fusion-based visual sensing and
perception strategy to perform accurate fruit localisation for a harvesting
robot in the apple orchards. Two SOTA extrinsic calibration methods,
target-based and targetless-based, are applied and evaluated to obtain the
accurate extrinsic matrix between the LiDAR and camera. With the extrinsic
calibration, the point clouds and color images are fused to perform fruit
localisation using a one-stage instance segmentation network. Experimental
shows that LiDAR-camera achieves better quality on visual sensing in the
natural environments. Meanwhile, introducing the LiDAR-camera fusion largely
improves the accuracy and robustness of the fruit localisation. Specifically,
the standard deviations of fruit localisation by using LiDAR-camera at 0.5 m,
1.2 m, and 1.8 m are 0.245, 0.227, and 0.275 cm respectively. These measurement
error is only one one fifth of that from Realsense D455. Lastly, we have
attached our visualised point cloud to demonstrate the highly accurate sensing
method.
