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Mobile robots designed for agricultural tasks need to deal with challenging outdoor unstructured environments that usually have dynamic and static obstacles. This assumption significantly limits the number of mapping, path planning, and navigation algorithms to be used in this application. As a representative case, the autonomous lawn mowing robot considered in this work is required to determine the working area and to detect obstacles simultaneously, which is a key feature for its working efficiency and safety. In this context, RGB-D cameras are the optimal solution, providing a scene image including depth data with a compromise between precision and sensor cost. For this reason, the obstacle detection effectiveness and precision depend significantly on the sensors used, and the information processing approach has an impact on the avoidance performance. The study presented in this work aims to determine the obstacle mapping accuracy considering both hardware- and information processing-related uncertainties. The proposed evaluation is based on artificial and real data to compute the accuracy-related performance metrics. The results show that the proposed image and depth data processing pipeline introduces an additional distortion of 38 cm.

This study investigates the impacts of Ground Control Points (GCP) reliability, quantity, and distribution on the accuracy of Unmanned Aerial Vehicle (UAV) photogrammetric 3D mapping. Previous studies have predominantly focused on overall accuracy metrics, often overlooking local variations in error distribution and failing to conduct thorough analysis of error sources, potentially resulting in inefficiencies and reduced photogrammetric 3D mapping accuracy due to sub-optimal GCP deployment and processing strategies. By employing a structured framework, this research analyzes GCP roles based on variations in their reliability. Aerial images of undulated terrains were analyzed and fifteen distinct GCP configurations were designed which incorporated various distributions and quantities. Random noise was added to simulate GCP reliability unpredictability, subsequently analyzed through a multifaceted approach. The results indicate that GCP reliability influences the upper limit of photogrammetric 3D mapping accuracy and significantly affects the influence of GCP quantity and distribution. It was also found that: when GCP reliability is high (RMSE within 0.1 m; density more than 10 GCP/ ${\\rm{k}}{{\\rm{m}}^2}$ k m 2 ), GCP number should prioritize over distribution, ie. accuracy improving as the number of GCPs increases; when GCP reliability is moderated (RMSE within 5 m; density around 5-10 GCP/ ${\\rm{k}}{{\\rm{m}}^2}$ k m 2 ), each of the uniform and distributed-edge distributions leads in different conditions; when GCP reliability is low (RMSE beyond 5 m; density less than 5 GCP/ ${\\rm{k}}{{\\rm{m}}^2}$ k m 2 ) it is favorable to use a distributed-edge and center distribution. Errors typically originate in edge areas and regions with significant topographic variations and propagating as GCP reliability decreases. Therefore, additional GCPs should be placed in error-prone regions to improve the accuracy.

Low-cost sensor solutions such as smartphones provide a great opportunity for democratization of mapping among different communities including those working in digital twin application areas. Smartphone acquired imagery and/or built-in LiDAR sensors provide relatively dense point clouds with limited accuracy especially in the absence of GNSS. This type of scanning tool can provide linear measurements in an inexpensive way and can be used with minimal operator training. In this study, we provide two solutions for improving the accuracy of the final point clouds produced by iPhone-based LiDAR and images. One solution utilizes length observations as constraints in the network. The other solution incorporates loosely coupled perspective centre (PC) positions obtained by ultrasonic ranging into a photogrammetric bundle adjustment. The test results show that inclusion of the length observations in the solution improves the relative accuracy of the point cloud for applications such as culvert mapping where absolute accuracy is not of high necessity. In the indoor mapping case, the relative point cloud accuracy for the solutions without and with the PC observations is approximately the same. However, image alignment success and computation time are significantly improved by including the PC observations. Moreover, the inclusion of PC observations provided better compensation of systematic image point errors.

Nucleotide conversion RNA sequencing techniques interrogate chemical RNA modifications in cellular transcripts, resulting in mismatch-containing reads. Biases in mapping the resulting reads to reference genomes remain poorly understood. We present splice_sim, a splice-aware RNA-seq simulation and evaluation pipeline that introduces user-defined nucleotide conversions at set frequencies, creates mixture models of converted and unconverted reads, and calculates mapping accuracies per genomic annotation. By simulating nucleotide conversion RNA-seq datasets under realistic experimental conditions, including metabolic RNA labeling and RNA bisulfite sequencing, we measure mapping accuracies of state-of-the-art spliced-read mappers for mouse and human transcripts and derive strategies to prevent biases in the data interpretation.

In this paper, we address the problem of creating an objective benchmark for evaluating SLAM approaches. We propose a framework for analyzing the results of a SLAM approach based on a metric for measuring the error of the corrected trajectory. This metric uses only relative relations between poses and does not rely on a global reference frame. This overcomes serious shortcomings of approaches using a global reference frame to compute the error. Our method furthermore allows us to compare SLAM approaches that use different estimation techniques or different sensor modalities since all computations are made based on the corrected trajectory of the robot. We provide sets of relative relations needed to compute our metric for an extensive set of datasets frequently used in the robotics community. The relations have been obtained by manually matching laser-range observations to avoid the errors caused by matching algorithms. Our benchmark framework allows the user to easily analyze and objectively compare different SLAM approaches.

This study compares the mapping accuracy of a non-RTK ultra-lightweight drone (DJI Mini2) with two survey-grade RTK-enabled drones (DJI Mavic3E and Phantom4) in three different sites. Flight parameters and weather conditions were the same on each site. The outputs were orthomosaics and digital surface models, whose accuracies were inspected by descriptive statistics and variance analysis tools. The data of the ultralight drone on the first site could not be processed due to strong wind, but its results for the second site (11 hectares) were comparable to those of survey-grade drones, i.e., the range and average of checkpoint errors for Mini2 were 0.17 m and 0.04 m, respectively, while those were 0.10 m and 0.02 m for Phantom4 and Mavic3E. In the third site (34 hectares), survey-grade drones produced accurate results with a checkpoint error range of 0.26 m, while that was 0.87 m for the ultralight drone, implying lower accuracy results. The results obtained suggest that ultralight drones under certain circumstances can produce reliable mapping products depending on weather conditions, the number and distribution of ground control points, and area size. Their biggest drawback is their vulnerability to wind, and in calm weather conditions, due to non-RTK error accumulation, their mapping accuracy degenerates as the area size increases.

The contribution of oil palm plantations to the economic growth of tropical developing countries makes it essential to monitor their expansion into the tropical forest; consequently, most studies focus on improving mapping accuracy while using satellite imagery. However, accuracy can be hampered by atmospheric phenomena that can drastically change climatic conditions in tropical regions, affecting the spectral properties of the vegetation. In this sense, we studied the accuracy of palm plantation mapping by using features from different regions of the electromagnetic spectrum and a data fusion approach, and then compared the changes in accuracy over the years 2016, 2017, and 2018 (two of them with reported climatic anomalies). Optical-based maps obtained higher accuracy than thermal- and microwave-based maps, but they were the most affected by inter-annual climate variability (error margin between 5 and 10%), while thermal-based maps were the least affected (error margin between 8 and 9%). Data fusion combinations improved accuracy and reduced dissimilarities between years (e.g., phenology-based map accuracy changed by up to 20.8%, while phenology fused with microwave features changed by up to 6.8%). We conclude that inter-annual climate variability on land-cover mapping should be considered, especially if the outputs will be used as input in future studies.

The use of airborne-mapping technology plays a key role in the acquisition of large-scale basic geographic data information, especially in various important civil/military-mapping missions. However, most airborne-mapping cameras are limited by parameters, such as the flight altitude, working-environment temperature, and so on. To solve this problem, in this paper, we designed a panchromatic wide-spectrum optical system with a focusing function. Based on the catadioptric optical structure, the optical system approached a telecentric optical structure. Sharp images at different object distances could be acquired by micro-moving the focusing lens. At the same time, an optical passive compensation method was adopted to realize an athermalization design in the range of −40–60 °C. According to the design parameters of the optical system, we analyzed the influence of system focusing on mapping accuracy during the focusing process of the airborne-mapping camera. In the laboratory, the camera calibration and imaging experiments were performed at different focusing positions. The results show that the experimental data are consistent with the analysis results. Due to the limited experiment conditions, only a single flight experiment was performed. The results show that the airborne-mapping camera can achieve 1:5000 scale-imaging accuracy. Flight experiments for different flight altitudes are being planned, and the relevant experimental data will be released in the future. In conclusion, the airborne-mapping camera is expected to be applied in various high-precision scale-mapping fields.

It is common practice for unmanned aerial vehicle (UAV) flight planning to target an entire area surrounding a single rooftop’s photovoltaic panels while investigating solar-powered roofs that account for only 1% of the urban roof area. It is very hard for the pre-flight route setting of the autopilot for a specific area (not for a single rooftop) to capture still images with high overlapping rates of a single rooftop’s photovoltaic panels. This causes serious unnecessary data redundancy by including the surrounding area because the UAV is unable to focus on the photovoltaic panel installed on the single rooftop. The aim of this research was to examine the suitability of a UAV video stream for building 3-D ortho-mosaics focused on a single rooftop and containing the azimuth, aspect, and tilts of photovoltaic panels. The 3-D position accuracy of the video stream-based ortho-mosaic has been shown to be similar to that of the autopilot-based ortho-photo by satisfying the mapping accuracy of the American Society for Photogrammetry and Remote Sensing (ASPRS): 3-D coordinates (0.028 m) in 1:217 mapping scale. It is anticipated that this research output could be used as a valuable reference in employing video stream-based ortho-mosaics for widely scattered single rooftop solar panels in urban settings.

Georeferenced archival aerial images are key elements for the study of landscape evolution in the scope of territorial planning and management. The georeferencing process proceeds by applying to photographs advanced digital photogrammetric techniques integrated along with a set of ground truths termed ground control points (GCPs). At the end of that stage, the accuracy of the final orthomosaic is assessed by means of root mean square error (RMSE) computation. If the value of that index is deemed to be unsatisfactory, the process is re-run after increasing the GCP number. Unfortunately, the search for GCPs is a costly operation, even when it is visually carried out from recent digital images. Therefore, an open issue is that of achieving the desired accuracy of the orthomosaic with a minimal number of GCPs. The present paper proposes a geostatistically-based methodology that involves performing the spatialization of the GCP errors obtained from a first gross version of the georeferenced orthomosaic in order to produce an error map. Then, the placement of a small number of new GCPs within the sub-areas characterized by the highest local errors enables a finer georeferencing to be achieved. The proposed methodology was applied to 67 historical photographs taken on a geo-morphologically complex study area, located in Southern Italy, which covers a total surface of approximately 55,000 ha. The case study showed that 75 GCPs were sufficient to garner an orthomosaic with coordinate errors below the chosen threshold of 10 m. The study results were compared with similar works on georeferenced images and demonstrated better performance for achieving a final orthomosaic with the same RMSE at a lower information rate expressed in terms of nGCPs/km2.