Three-dimensional (3D) object detection has important applications in robotics, automatic loading, automatic driving and other scenarios. With the improvement of devices, people can collect multi-sensor/multimodal data from a variety of sensors such as Lidar and cameras. In order to make full use of various information advantages and improve the performance of object detection, we proposed a Complex-Retina network, a convolution neural network for 3D object detection based on multi-sensor data fusion. Firstly, a unified architecture with two feature extraction networks was designed, and the feature extraction of point clouds and images from different sensors realized synchronously. Then, we set a series of 3D anchors and projected them to the feature maps, which were cropped into 2D anchors with the same size and fused together. Finally, the object classification and 3D bounding box regression were carried out on the multipath of fully connected layers. The proposed network is a one-stage convolution neural network, which achieves the balance between the accuracy and speed of object detection. The experiments on KITTI datasets show that the proposed network is superior to the contrast algorithms in average precision (AP) and time consumption, which shows the effectiveness of the proposed network.

The relationship between the axial structures of three-dimensional (3-D) targets and the signal of the remote-sensing techniques, such as the modulated echo waveform of the full-waveform light detection and ranging (FW-LiDAR), has been well established. However, the relationship between the lateral structures on modulated echo waveform has not been exploited in detail. For FW-LiDAR, the peak intensity, rather than the shape and width, of the echo waveform reflects lateral structural information. Using four typical two-dimensional shapes to approach the lateral structures, a mathematical formula bridging the peak intensity and the lateral structures is derived. Based upon the echo waveform simulated using the formula, modulations of the lateral structural information on the peak intensity are examined to establish a database of target properties, including shape, size and position. The physics regarding peak intensity dependence on target position is used for facile shape recognition. The modulation of the lateral shapes of ground and aerial targets on the FW-LiDAR can be assessed extensively following the procedure demonstrated in this paper. Owing to the conciseness, the peak intensity formula enables the retrieval of lateral structural information by inversion, promoting the applications of FW-LiDAR in domains including topological mapping, ecological monitoring and space debris detection and removal.

In remote sensing domains, it is difficult to evaluate the lateral structures using the current remote sensing techniques. The mathematical peak intensity formula of the echo waveform modulated by the lateral structures establishes a quantitative yet concise relationship between the peak intensity and the lateral structures, enabling the retrieval of lateral structural details in terms of inverting the formula. The process of the retrieval includes: 1) mathematical formula derivation, II) target shape discrimination, and III) mathematical formula inversion. Using the sizes estimated from the simulated echo waveforms, this study demonstrates how the estimated lateral structures are affected by the number of lateral structural parameters to be solved, instrument noise, movement direction, target shape, and target size. The results reveal that for unknown target size and lateral structures, the averaged size errors are 0.56% and 4.30%, respectively. When the instrument noise is absent and only the target size is unknown, the size error averaged over four shapes is 0.3%, and that averaged over the square, circle, triangle is 0.04%. When only the size is unknown, the size errors of the rectangle, square, circle, and triangle estimated by fitting the experimental peak intensity with the formula are 2.41%, 3.47%, 0.89%, and 1.42%, respectively. The small size errors prove the possibility of retrieving the lateral sizes at a centimeter level resolution and a distance of hundreds of kilometers, which is of great practical significance in precisely mapping the lateral structures of 3-D targets using full-waveform light detection and ranging (FW-LiDAR).

针对远距离运动目标的光子测距问题, 建立了运动目标的光子探测回波概率分布模型, 给出了适用于任意目标的光子探测蒙特卡洛模型. 通过实验对比, 验证了蒙特卡洛仿真模型的正确性. 进一步分析了一个探测周期内的平动小矩形目标激光回波和光子回波概率分布变化规律, 讨论了光子测距误差与目标平动速度间的关系. 结果表明: 光斑直径为\begin{document}$ {\text{2}}{\text{.5 m}} $\end{document}、目标尺度为\begin{document}$ {\text{1 m}} $\end{document}时, 距离漂移在速度为\begin{document}$ 25{\text{ m/s}} $\end{document}取到极大值\begin{document}$ 6.72{\text{ cm}} $\end{document}, 是扩展目标距离漂移的\begin{document}$ 1/2 $\end{document}倍; 随着平动速度的增加, 以出光斑为界, 距离漂移先增大后保持稳定不变. 本文提出的方法可进一步扩展到其他形状、材质、姿态、运动目标的光子探测, 研究结果为运动目标的光子测距的校正和性能的提升提供了理论依据.

This paper reviews the WALES mission by the European Space Agency (ESA) and the HALO missions conducted by Langley Research Center (LaRC) of NASA. It evaluates potential risks associated with spaceborne applications by examining factors such as single-pulse energy, pulse repetition frequency, optical frequency stability, efficiency, spectral purity, and reliability. The review also explores advancements in solid-state laser technology operating at 820 nm over the past two decades, as well as recent developments in diode-pumped Er:YAG lasers. Key innovations from these technologies have been incorporated into differential absorption lidar transmitters to enhance performance. Separating weather forecasting from climate-change research is recommended for spaceborne missions. The 935 nm spectral band is optimal for water vapor DIAL measurements in the upper troposphere and lower stratosphere (UTLS). In contrast, the 820 nm band is ideal for measurements in the lower troposphere.