Various error factors influence the inversion of forest canopy height using GEDI full-waveform LiDAR data, and the interaction of these factors impacts the accuracy of forest canopy height estimation. From an error perspective, there is still a lack of methods to fully correct the impact of various error factors on the retrieval of forest canopy height from GEDI. From the modeling perspective, establishing clear coupling models between various environments, collection parameters, and GEDI forest canopy height errors is challenging. Understanding the comprehensive impact of various environments and collection parameters on the accuracy of GEDI data is crucial for extracting high-quality and precise forest canopy heights. First, we quantitatively assessed the accuracy of GEDI L2A data in forest canopy height inversion and conducted an error analysis. A GEDI forest canopy height error correction model has been developed, taking into account both forest density and terrain effects. This study elucidated the influence of forest density and terrain on the error in forest canopy height estimation, ultimately leading to an improvement in the accuracy of forest canopy height inversion. In light of the identified error patterns, quality control criteria for GEDI footprints are formulated, and a correction model for GEDI forest canopy height is established to achieve high-precision inversion. We selected 19 forest areas located in the United States with high-accuracy Digital Terrain Models (DTMs) and Canopy Height Models (CHMs) to analyze the error factors of GEDI forest canopy heights and assess the proposed accuracy improvement for GEDI forest canopy heights. The findings reveal a decrease in the corrected RMSE value of forest canopy height from 5.60 m to 4.19 m, indicating a 25.18% improvement in accuracy.

Millimeter wave (MMW) radar simultaneous localization and mapping (SLAM) technology is an emerging technology in a tunnel vehicle accident rescue scene. It is a powerful tool for statistic-trapped vehicle detection with limited vision caused by darkness, heat, and smoke. A variety of SLAM frameworks have been proven to be able to obtain 3-degree-of-freedom (3-dof) pose estimation results using 2-dimention (2D) MMW radar in open space. In the application of millimeter wave radar for pose estimation and mapping in a closed environment, closed space structures and artificial targets together constitute high-intensity multi-path scattering measurement data, leading to radar false detections. Radar false detections caused by multi-path scattering are generally considered to be detrimental to radar applications, such as multi-target tracking. However, few studies analyze the mechanism of multi-path effects on radar SLAM, especially in closed spaces. In order to address the problem, this paper first presents a radar multi-path scattering theory to study the generation mechanism difference of false and radar true detection and their influences on radar SLAM 2D pose estimation and mapping in tunnel. According to the scattering mechanism differences on SLAM, a radar azimuth scattering angle signature is proposed, which allows distinguishing radar false detections from real ones. This is useful in avoiding using unreliable radar false detections to solve a radar SLAM problem. In addition, two different radar false detection revising methods combined with the CSM (correlative scan matching) algorithm are proposed in this paper. The HTMR-CSM (hard-threshold-multi-path-revised correlative scan matching) algorithm only depends on a hard threshold of radar azimuth scattering angle signature to eliminate all radar false detections as much as possible before CSM. Another idea is the STMR-CSM (soft-threshold-multi-path-revised correlative scan matching) algorithm. All the radar false detections are classified according to the distribution model of radar azimuth accuracy, and part of more reliable radar false detections are retained to estimate a more accurate pose. All the ideas in this paper are validated by using an MMW 2D radar mounted on a rail-guided robot in a tunnel. Two cars on fire were set as the targets. The experimental results show that the STMR-CSM algorithm that keeps the reliable radar false detections improves the positioning accuracy by 20% compared with CSM.

Millimeter-wave (MMW) radar Simultaneous Localization and Mapping (SLAM) technology is a powerful tool for vehicle detection with limited vision in fire scenario caused by underground road vehicle accident. Current approaches based on the maximum likelihood radar movement locus estimation showed promising results. However, the random error of radar azimuth angle measurement is accumulated and took into radar movement locus estimation due to coordinate transformation and a regular radar scattering pattern distribution caused by a regular radar movement pattern. It also makes an anamorphic occupancy grid map leading to a worse target detection performance. This paper attempts to integrate a prior rail-motion model into the current approach for reducing the accumulated error. This algorithm is validated by the test data measured by a MMW radar mounted on a rail-guided robot in a tunnel. Two cars on fire were set as the targets. The experiment proves the effectiveness of the approach and the error of radar movement locus estimation is less than 0.1m (radar LOS direction) and 1.8m (radar movement direction) respectively.