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Lidar is an active remote sensing instrument, but its effective range is often limited by signal-to-noise (SNR) ratio. The reason is that noises or fluctuations always strongly affect the measured results. To resolve this problem, a novel approach of using least-squares support vector machine (LS-SVM) to reconstruct the Lidar signal is proposed in this paper. LS-SVM has been proven as robust to noisy data; the Lidar signal, which is strongly corrupted by noises or fluctuations, can be thought as a function of distance. So detecting Lidar signals from high noisy regime can be regarded as a robust regression procedure which involves estimating the underlying relationship from detected signal data set. To apply the LS-SVM on Lidar signal regression, firstly the noises in Lidar signal is analyzed and then the traditional LS-SVM algorithm is modified to incorporate the a priori knowledge of the Lidar signal in the training of LS-SVM. The experimental results demonstrate the effectiveness and efficiency of our approach.

Automated unmanned vehicles can carry equipment for teams, greatly reducing the load of person. Most of the working environment of unmanned vehicles is in the field. The identification of persons in a cross-country environment is the basic requirement for automated driving vehicles, and the problem of identification has received much attention. Aiming at the problem of person identification from lidar point cloud data, particularly the special problem of identification in a cross-country environment, this paper designs an improved identification algorithm based on Euclidean clustering, theoretical analysis, and the geometric and physical characteristics of people. Experiments are carried out on tracked vehicle platforms in a cross-country environment to verify the performance of the algorithm. The experimental results show that the designed lidar personnel identification algorithm can accurately identify personnel using lidar point cloud data, and the recognition rate is about 5% higher than that before the improvement.

This paper describes and compares three different approaches to estimate simultaneous localization and mapping (SLAM) in dynamic outdoor environments. SLAM has been intensively researched in recent years in the field of robotics and intelligent vehicles, many approaches have been proposed including occupancy grid mapping method (Bayesian, Dempster-Shafer and Fuzzy Logic), Localization estimation method (edge or point features based direct scan matching techniques, probabilistic likelihood, EKF, particle filter). In this paper, a number of promising approaches and recent developments in this literature have been reviewed firstly in this paper. However, SLAM estimation in dynamic outdoor environments has been a difficult task since numerous moving objects exist which may cause bias in feature selection problem. In this paper, we proposed a possibilistic SLAM with RANSAC approach and implemented with three different matching algorithms. Real outdoor experimental result shows the effectiveness and efficiency of our approach.

This chapter introduces a novel approach to tree detection by fusing LiDAR (Light Detection and Ranging) and RGB imagery, leveraging Ordered Weighted Averaging (OWA) aggregation operators to improve image fusing. It focuses on enhancing tree detection and classification by combining LiDAR’s structural data with the spectral details from RGB images. The fusion methodology aims to optimize information retrieval, employing image segmentation and advanced classification techniques. The effectiveness of this method is demonstrated on the PNOA dataset, highlighting its potential for supporting forest management.

Air density online measurement lidar paraxial imaging receiving optical system is designed according to requirements on high altitude rarefied atmosphere density real-time online measurement. Initial structure form of combining Ernostar objective lens and Tessar objective lens is adopted for the receiving objective lens in the system. Laser backward scattering optical imaging in the short-range 180 mm-3000 mm measurement area is realized under the precondition of ensuring that the receiving system has larger caliber, relative aperture and smaller volume. Aberration analysis results and test results based on ZEMAX software are provided. Results show that the imaging quality of designed objective can satisfy the requirements on online measurement of atmospheric density. Air density measurement precision with the lidar developed by the design institute is higher than 5%.