The generation of dense and accurate point clouds from airborne light detection and ranging (LiDAR) waveform data is crucial to forest inventory. This work proposes a deconvolution method with: 1) an automatic stopping criterion to differentiate near-adjacent targets and 2) an iterative false subwaveform removal algorithm to remove outliers caused by noise. Synthetic waveforms with different overlap rates were processed using the proposed method, the Gaussian decomposition (GD) method, and the Richardson Lucy (RL) deconvolution method. Results showed that: 1) the number of subwaveforms detected by the proposed method is 9% higher than that of the RL and 20% higher than that of the GD when the overlap rates are larger than 0.6 and 2) the proposed method is of the smallest ground and peak distance errors. Results of the indoor experiment also show that the proposed method is superior in finding near targets meanwhile leading to small ground and peak distance error. Furthermore, the proposed method was tested by airborne waveforms from the Dagujia forest farm. The point cloud density acquired by the proposed method is 3% and 35% higher than that by the RL and GD method. Fewer outliers are produced by the proposed method. The number of individual trees extracted from the proposed point clouds is 22%, 51%, and 57% greater than those extracted from the RL, the GD, and the reference point clouds using the canopy height model-based method. Best individual tree extraction result is produced by the proposed method, especially for an area with small trees.

Waveform decomposition is the key step in acquiring targets’ 3-D information using full-waveform light detection and ranging (LiDAR). With an attempt to effectively detecting targets with low signal-to-noise ratio (SNR), a B-spline based progressive decomposition method was proposed. This method uses B-spline to precisely model waveforms and uses progressive decomposition method to suppress noise influences. Simulation and ranging experiment were implemented to verify the proposed method at a sampling rate of 1 GSa/s with SNR varies from 10 to 30 dB and target interval varies from 1 to 2 m. Ranging experiment results show that the target extraction rate of the proposed method is the most accurate. The proposed method has strong target detection ability that it can effectively detect 90% targets since 14 dB even when the two targets are as close as 1 m with lowest underfitting rate. Robustness against noise of the proposed method is proven by its overfitting rate, which maintains no more than 5.5% at all tested conditions. Further comparison shows that the ranging accuracy of the proposed method is no more than 11.82 cm and the ranging precision of the proposed method is 13.71%–40.68% smaller than the deconvolution methods and Gaussian decomposition when target interval ranges from 1 to 1.25 m. Comparison results demonstrate the high ranging trueness and repeatability of the proposed method. The proposed method can be effectively applied to target detection from waveforms with low SNR for improving long ranging performances.

Accurate and robust visual localization is essential for autonomous lunar landing. This study presents a new crater-based method that addresses challenges posed by environmental uncertainties such as camera pose deviations, the number of craters within the scene, and the image brightness. Our method combines crater Geohash encoding for efficient database retrieval with an improved principal component analysis (PCA) for crater detection. The detected craters are ranked, retaining those with fewer but more accurate detections to meet localization requirements. Crucially, we introduce a consistency matching technique that exploits the linear relationship between position shifts and pixel offsets, enhancing both localization accuracy and computational efficiency. Experimental results across diverse scenes and simulation conditions demonstrate 100% matching accuracy with an average matching time under 0.8 s. Reprojection errors remain below 3 px, significantly outperforming methods like triangle similarity matching (TSM) and direct matching (DM). This validates the proposed method’s high precision and stability for near real-time lunar localization.

Crater-based terrain relative navigation (TRN) can be used for precise pinpoint landing in lunar landing missions. However, the existing crater detection algorithms (CDAs) cannot accurately extract the parameters of the crater. To address these issues, this paper presents an accurate crater detection method that is based on dark–bright boundary identification. First, a correlation template is generated from selecting sample images for craters of interest. Then, a dark–bright boundary identification method based on local correlation max–min matching is proposed to obtain the boundary of the crater. Finally, the diameter and position of craters are determined by circle fitting from boundary points. With images from the Lunar Reconnaissance Orbiter Camera (LROC) Wide Angle Camera (WAC) and Narrow Angle Camera (NAC) in different scenes, the experimental results demonstrate that the proposed algorithm reduces the position error by 27.45% and the diameter error by 53.02% compared with the PCA-based algorithm. Thus, the presented method can detect craters parameter accurately from navigation images for TRN.

With the growing demand for precise absolute pose estimation of landers in lunar exploration missions, crater database-based navigation technology has become a core path to achieving this goal, but it faces challenges of low efficiency in large-scale data retrieval and insufficient matching robustness. To address these issues, a coarse-to-fine crater matching framework with database fast searching and robust triangle similarity matching is proposed. A Geo-KD search algorithm is designed to realize fast and accurate retrieval of craters within the field of view by combining Geohash and KD-tree. A robust triangle similarity matching algorithm is constructed through local neighborhood crater screening, triangle similarity matching, and mismatching elimination based on Random Sample Consensus (RANSAC) and Local Motion Consistency (LMC). Experiments show that the algorithm achieves an average retrieval time of 20 ms with an F₁-score of 0.8 for the global lunar database with 1.29 million craters. It has an F₁-score more than 0.746 and a single-frame matching time less than 1.005 s under lunar orbital phase, landing phase, and different camera pitch angles, outperforming other advanced algorithms and meeting on-orbit real-time requirements, providing reliable support for the absolute pose estimation of lunar probes.