Monocular vision is one of the most commonly used noncontact six-degrees-of-freedom (6-DOF) pose estimation methods. However, the large translational DOF measurement error along the optical axis of the camera is one of its main weaknesses, which greatly limits the measurement accuracy of monocular vision measurement. In this paper, we propose a novel monocular camera and 1D laser rangefinder (LRF) fusion strategy to overcome this weakness and design a remote and ultra-high precision cooperative targets 6-DOF pose estimation sensor. Our approach consists of two modules: (1) a feature fusion module that precisely fuses the initial pose estimated from the camera and the depth information obtained by the LRF. (2) An optimization module that optimizes pose and system parameters. The performance of our proposed 6-DOF pose estimation method is validated using simulations and real-world experiments. The experimental results show that our fusion strategy can accurately integrate the information of the camera and the LRF. Further optimization carried out on this basis effectively reduces the measurement error of monocular vision 6-DOF pose measurement. The experimental results obtained from a prototype show that its translational and rotational DOF measurement accuracy can reach up to 0.02 mm and 15″, respectively, at a distance of 10 m.

Recent advances in the fields of driverless cars, intelligent robots and remote-sensing measurement have shown that the use of LiDAR fused with cameras can provide more comprehensive and reliable sensing of surroundings. However, since it is difficult to extract features from sparse LiDAR data to create 3D–2D correspondences, finding a method for accurate external calibration of all types of LiDAR with cameras has become a research hotspot. To solve this problem, this paper proposes a method to directly obtain the 3D–2D correspondences of LiDAR–camera systems to complete accurate calibration. In this method, a laser detector card is used as an auxiliary tool to directly obtain the correspondences between laser spots and image pixels, thus solving the problem of difficulty in extracting features from sparse LiDAR data. In addition, a two-stage framework from coarse to fine is designed in this paper, which not only can solve the perspective-n-point problem with observation errors, but also requires only four LiDAR data points and the corresponding pixel information for more accurate external calibration. Finally, extensive simulations and experimental results show that the effectiveness and accuracy of our method are better than existing methods.

The satellite, Ice, Cloud and Land Elevation Satellite-2 (ICESat-2) has been equipped with a new type of spaceborne laser altimeter, which has the benefits of having small footprints and a high repetition rate, and it can produce dense footprints on the ground. Focusing on the pointing angle calibration of this new spaceborne laser altimeter, this paper proposes a fast pointing angle calibration method using only a small range of terrain surveyed by airborne lidar. Based on the matching criterion of least elevation difference, an iterative pointing angle calibration method was proposed. In the experiment, the simulated photon-counting laser altimeter data and the Ice, Cloud and Land Elevation Satellite-2 data were used to verify the algorithm. The results show that when 1 km and 2.5 km lengths of track were used, the pointing angle error after calibration could be reduced to about 0.3 arc-seconds and less than 0.1 arc-seconds, respectively. Meanwhile, compared with the traditional pyramid search method, the proposed iterative pointing angle calibration method does not require well-designed parameters, which are important in the pyramid search method to balance calculation time and calibration result, and the iterative pointing angle calibration method could significantly reduce the calibration time to only about one-fifth of that of the pyramid search method.

In this paper, a simple and easy high-precision calibration method is proposed for the LRF-camera combined measurement system which is widely used at present. This method can be applied not only to mainstream 2D and 3D LRF-cameras, but also to calibrate newly developed 1D LRF-camera combined systems. It only needs a calibration board to record at least three sets of data. First, the camera parameters and distortion coefficients are decoupled by the distortion center. Then, the spatial coordinates of laser spots are solved using line and plane constraints, and the estimation of LRF-camera extrinsic parameters is realized. In addition, we establish a cost function for optimizing the system. Finally, the calibration accuracy and characteristics of the method are analyzed through simulation experiments, and the validity of the method is verified through the calibration of a real system.