Robot calibration is used to improve the accuracy of the kinematic model to achieve the higher positioning accuracy within the workspace. Due to some nongeometrical reasons such as joint and link flexibility, the errors are unevenly distributed in the workspace. In this case, it is difficult for the existing methods used to improve the absolute positioning accuracy to achieve good results in each region, especially for robots with large self‐weights. In this paper, a novel calibration method is proposed, which deals with joint deflection dependent errors to enhance the robot positioning accuracy in the whole workspace. Firstly, the joint angle workspace is divided into several local regions according to the mass distribution of the robot. Then, its geometric parameters are modeled and identified using the Denavit–Hartenberg (DH) model in each region and in the whole workspace separately. Since the nongeometric error sources are difficult to model correctly, an artificial neural network (ANN) is applied to compensate for the nongeometric errors. Finally, the experiments using an 8 degree‐of‐freedom (DOF) engineering robot are conducted to demonstrate the validity of the proposed method. The combination of the joint angle division and ANN could be an effective solution for the robot calibration, especially for one with a large self‐weight.

To address the issue that sparse point cloud maps constructed by SLAM cannot provide detailed information about measured objects, and image sequence-based measurement methods have problems with large data volume and cumulative errors, this paper proposes a scene measurement method that integrates image sequences with an improved LiDAR SLAM. By introducing plane features, the positioning accuracy of LiDAR SLAM is enhanced, and real-time odometry poses are generated. Simultaneously, the system captures image sequences of the measured object using synchronized cameras, and NeRF is used for 3D reconstruction. Time synchronization and data registration between the LiDAR and camera data frames with identical timestamps are achieved. Finally, the least squares method and ICP algorithm are employed to compute the scale factor s and transformation matrices R and t between different point clouds from LiDAR and NeRF reconstruction. Then, the precise measurement of the objects could be implemented. Experimental results demonstrate that this method significantly improves measurement accuracy, with an average error within 10 mm and 1°, providing a robust and reliable solution for scene measurement.