In this work, we propose a new light detection and ranging (LiDAR) super-resolution method for indoor and outdoor scenes in urban environments without training, converting to images and auxiliary sensors. To generate high-resolution (high-res) point clouds from the low-resolution (low-res) measurement of a sparse LiDAR, we deconstruct the whole problem into three sequential modules. First, to avoid feature interference, the raw data are segmented into the ground and nonground points. Then, these two types of points are reorganized into a more regular representation of the coordinates. Finally, the high-res point clouds are generated via a new geometric method. We present abundant synthetic and real data based on testing and evaluation of the proposed method. Qualitative and quantitative comparisons show that our method is effective and robust.

We propose an innovative method with which to extract building interior structure information automatically, including ceiling, floor, and wall. Our approach outperforms previous methods in the following respects. First, we propose an approach based on principal component analysis (PCA) to find the ground plane, which is regarded as the new Cartesian plane. Second, to reduce the complexity of data processing, the data are projected into two dimensions and transformed into a binary image via the operation of an improved radius outlier removal (ROR) filter. Third, a traditional thinning algorithm is adopted to extract the image skeleton. Then, we propose a method for calculating slope through the nearest neighbor point. Moreover, the line is represented with the slopes to obtain information pertaining to the interior planes. Finally, the outline of the line is restored to a three-dimensional structure. The proposed method is evaluated in multiple scenarios, and the results show that the method is accurate (the maximum error of 0.03 m was in three scenarios) in indoor environments.

To improve the efficiency and accuracy of plane segmentation for indoor point clouds, this paper proposes a fast and precise plane segmentation framework which mainly consists of two steps: plane rough segmentation and precise segmentation. In the rough segmentation stage, the point clouds are firstly voxelized, then the original plane is extracted roughly according to the plane normal vector and nearest voxels conditions. Based on the results of rough segmentation, a further operation composed of downsampling and density-based spatial clustering of applications with noise (DBSCAN) is adopted to produce efficient and precise segmentation. Finally, to correct the over-segmentation, the distance and normal vector angle thresholds between planes are taken into consideration. The experimental results show that the proposed method improves the efficiency and accuracy of indoor point cloud plane segmentation, and the average intersection-over-union (IoU) achieves 0.8653.

Infrared (IR) small target detection faces two major challenges: single-frame-based methods are highly sensitive to random noise, while multi-frame-based approaches often neglect the dynamic nature of background scenes, resulting in high false alarm rates. To address these issues, this paper proposes a novel ring local contrast measure weighted by spatial–temporal prior (WSTRLCM). WSTRLCM comprises two key modules: a spatial-temporally enhanced ring local contrast and a spatial-temporal weighting factor. Specifically, a spatial–temporal enhancement scheme is first applied to the original image sequence to boost target saliency, and the ring contrast computation is optimized for improved efficiency. For the weighting factor, a small target enhancement strategy based on image block gradients is introduced. By incorporating structure tensor analysis, effective back-ground suppression is achieved, resulting in a refined spatial-temporal enhancement sequence. Furthermore, a tailored multi-frame difference approach, designed according to target motion characteristics, is employed to generate the final weighting factor. The Hadamard product of the local contrast and the weighting factor is then computed, and adaptive thresholding is applied to obtain the final detection map. Experimental results on public datasets demonstrate that WSTRLCM achieves superior performance compared to several state-of-the-art methods, exhibiting enhanced target detection capability, strong background suppression. The code is available at https://github.com/biangbiangliang/WSTRLCM.

Pedestrian inertial navigation is a pivotal technology for achieving seamless indoor and outdoor positioning. Traditional methods based on the Extended Kalman Filter (EKF) suffer from cumulative errors induced by inertial measurement unit (IMU) noise, which severely degrade the accuracy of pedestrian trajectory estimation over long durations. To address this critical limitation, a post-processing trajectory optimization approach for pedestrian inertial navigation based on the Birkhoff pseudospectral method is proposed in this paper. Leveraging the initial attitude and position estimates derived from the Zero-Velocity Update (ZUPT) technique and the EKF framework, the proposed method first parameterizes continuous-time acceleration measurements by adopting Chebyshev nodes as collocation points, and then formulates and solves the trajectory optimization problem via a Birkhoff pseudospectral framework, which effectively suppresses noise interference from the IMU accelerometer. Simulation experiments validate the superior noise suppression capability of the proposed algorithm. Furthermore, physical experiments conducted with a foot-mounted IMU demonstrate that the final position error is reduced by approximately 90% in comparison with the traditional EKF-based method.