Mapping forests using an unmanned ground vehicle with 3D LiDAR and graph-SLAM

Enabling automated 3D mapping in forests is an important component of the future development of forest technology, and has been garnering interest in the scientific community, as can be seen from the many recent publications. Accordingly, the authors of the present paper propose the use of a Simultaneous Localisation and Mapping algorithm, called graph-SLAM, to generate local maps of forests. In their study, the 3D data required for the mapping process were collected using a custom-made, mobile platform equipped with a number of sensors, including Velodyne VLP-16 LiDAR, a stereo camera, an IMU, and a GPS. The 3D map was generated solely from laser scans, first by relying on laser odometry and then by improving it with robust graph optimisation after loop closures, which is the core of the graph-SLAM algorithm. The resulting map, in the form of a 3D point cloud, was then evaluated in terms of its accuracy and precision. Specifically, the accuracy of the fitted diameter at breast height (DBH) and the relative distance between the trees were evaluated. The results show that the DBH estimates using the Pratt circle fit method could enable a mean estimation error of approximately 2 cm (7–12%) and an RMSE of 2.38 cm (9%), whereas for tree positioning accuracy, the mean error was 0.0476 m. The authors conclude that robust SLAM algorithms can support the development of forestry by providing cost-effective and acceptable quality methods for forest mapping. Moreover, such maps open up the possibility for precision localisation for forestry vehicles.publishedVersio

Ground Profile Recovery from Aerial 3D LiDAR-based Maps

The paper presents the study and implementation of the ground detection methodology with filtration and removal of forest points from LiDAR-based 3D point cloud using the Cloth Simulation Filtering (CSF) algorithm. The methodology allows to recover a terrestrial relief and create a landscape map of a forestry region. As the proof-of-concept, we provided the outdoor flight experiment, launching a hexacopter under a mixed forestry region with sharp ground changes nearby Innopolis city (Russia), which demonstrated the encouraging results for both ground detection and methodology robustness.Comment: 8 pages, FRUCT-2019 conferenc

Robotic navigation algorithm with machine vision

In the field of robotics, it is essential to know the work area in which the agent is going to develop, for that reason, different methods of mapping and spatial location have been developed for different applications. In this article, a machine vision algorithm is proposed, which is responsible for identifying objects of interest within a work area and determining the polar coordinates to which they are related to the observer, applicable either with a fixed camera or in a mobile agent such as the one presented in this document. The developed algorithm was evaluated in two situations, determining the position of six objects in total around the mobile agent. These results were compared with the real position of each of the objects, reaching a high level of accuracy with an average error of 1.3271% in the distance and 2.8998% in the angle

Comparison of low-cost handheld LiDAR-based SLAM systems for mapping underground tunnels

The use of mobile mapping technologies (MMT) has become increasingly popular across various applications such as forestry, cultural heritage, mining, and civil engineering. While Simultaneous Localization and Mapping (SLAM) algorithms have greatly improved in recent years with regards to accuracy, robustness, and cooperativity, it is important to understand the limitations and strengths of each metrological measurement method to ensure the provision of 3D data of appropriate quality for the selected application. In this study, we perform a comparative analysis of three LiDAR-based handheld mobile mapping systems with survey-grade reference point clouds in a challenging test area of a partially collapsed underground tunnel. We investigate various aspects of 3D data quality, including accuracy and completeness, and present an improved method for 3D data completeness assessment aimed at evaluating SLAM-derived point clouds. The results demonstrate unique and diverse strengths and shortcomings of the tested mapping systems, which provide valuable guidelines for selecting an appropriate system for subterranean applications

Automatic 3D Mapping for Tree Diameter Measurements in Inventory Operations

Forestry is a major industry in many parts of the world. It relies on forest inventory, which consists of measuring tree attributes. We propose to use 3D mapping, based on the iterative closest point algorithm, to automatically measure tree diameters in forests from mobile robot observations. While previous studies showed the potential for such technology, they lacked a rigorous analysis of diameter estimation methods in challenging forest environments. Here, we validated multiple diameter estimation methods, including two novel ones, in a new varied dataset of four different forest sites, 11 trajectories, totaling 1458 tree observations and 1.4 hectares. We provide recommendations for the deployment of mobile robots in a forestry context. We conclude that our mapping method is usable in the context of automated forest inventory, with our best method yielding a root mean square error of 3.45 cm for our whole dataset, and 2.04 cm in ideal conditions consisting of mature forest with well spaced trees

A fully automatic forest parameters extraction at single-tree level: a comparison of MLS and TLS applications

Forests are vital for ecological, economic, and social reasons, and adopting sustainable forest management practices is necessary. While traditional forest monitoring techniques provide detailed data, they are time-consuming; conversely, geomatic techniques can provide more detailed data for forest resource management. This study aims to assess the suitability of Mobile Mapping Systems (MMS) with simultaneous localisation and mapping (SLAM) technology for precision forestry purposes in challenging environments. We compared the performance of MMS data with Terrestrial Laser Scanning (TLS) data and evaluated the Forest Structural Complexity Tool (FSCT), which was developed for TLS datasets, on MMS data. The case study area is a highly sloped coniferous forest in the Italian Alps affected by a severe fire in 2017. Data were processed using a fully automated open-source Python tool that detects each tree's position, Diameter at Breast Height (DBH), and height. The validation procedure was conducted with respect to the TLS point cloud manually segmented. The results show that using MMS with SLAM technology is suitable for precision forestry purposes in challenging environments and that FSCT performs well on MMS data

Development of a smart system for early detection of forest fires based on unmanned aerial vehicles

The naturally occurring wildfires and the people-related forest fires are events, which in many cases have significant impact on the environment, the wildlife and the human population. The most devastating among these events usually start in unpopulated remote areas, which are difficult to inspect or are not constantly being monitored or observed. This gives the local small-sized fires enough time to evolve into full-scale wide-area disasters, which in turn makes their suppression and extinguishing very difficult. In this paper, we present an autonomous system for early detection of forest fires, named THEASIS-M. The presented system represents a solution that is based on a combination of innovative technologies, including computer vision algorithms, artificial intelligence and unmanned aerial vehicles. In the first part of the study, we provide an overview on the present applications of the UAVs in the forestry domain. The paper then introduces the general architecture of the THEASIS-M system and its components. The system itself is fully autonomous and is based on several different types of UAVs, including a fixed-wing drone, which provides the overall forest monitoring capabilities of the proposed solution, and a rotary-wing UAV that is used for confirmation and monitoring of the detected fire event. The widely used technologies for computer vision and image processing, which are used for the detection of fire and smoke in the real-time video streams sent from the UAVs to the ground control station, are highlighted in the next section of this study. Finally, the experimental tests and demonstrations of the proposed THEASIS-M system are presented and briefly discussed