Enhanced Algorithms for Estimating Tree Trunk Diameter Using 2D Laser Scanner

Ringdahl, Ola; Hohnloser, Peter; Hellström, Thomas; Holmgren, Johan; Lindroos, Ola

doi:10.3390/rs5104839

Cited by 24 publications

(27 citation statements)

References 26 publications

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“…However, with the existing tree map, it can be assumed that MS-STI is capable of providing robust estimates in various forest conditions, and we will investigate that in forthcoming studies. A tree map with species information is a challenging prerequisite, but the recent developments in mobile 2D and 3D laser scanning indicates that the solution is within reach [23][24][25][26][27][28][29][30]. In our study, the tested input tree map was produced by TLS and using GNSS with an expected accuracy of below 0.1 m. In operational forest management, tree mapping would be carried out after or during the thinning, which is the earliest stage to apply MS-STI.…”

Section: Discussionmentioning

confidence: 99%

“…These methods are capable of providing tree maps within one-meter accuracy [23,25,26,28]. It is suggested by many researchers [3,5,20,24,26,[28][29][30][31] that these ground-based laser sensors could be mounted to the harvesters to measure standing trees during the logging. The forest industry is also eager to develop harvester measurements and mount these 2D or 3D laser scanners to the harvester.…”

Section: Introductionmentioning

confidence: 99%

“…Spatially accurate tree maps would be a major step toward virtual 3D forests, and they would be beneficial in the planning of forest management operations and as input information for the next generation's growth models. Mobile 2D and 3D laser scanning methods have rapidly developed during recent years [23][24][25][26][27][28][29]. These methods are capable of providing tree maps within one-meter accuracy [23,25,26,28].…”

Section: Introductionmentioning

confidence: 99%

“…Mobile 2D and 3D laser scanning methods have rapidly developed during recent years [23][24][25][26][27][28][29]. These methods are capable of providing tree maps within one-meter accuracy [23,25,26,28]. It is suggested by many researchers [3,5,20,24,26,[28][29][30][31] that these ground-based laser sensors could be mounted to the harvesters to measure standing trees during the logging.…”

Section: Introductionmentioning

confidence: 99%

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Multisource Single-Tree Inventory in the Prediction of Tree Quality Variables and Logging Recoveries

et al. 2014

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Abstract:The stem diameter distribution, stem form and quality information must be measured as accurately as possible to optimize cutting. For a detailed measurement of the stands, we developed and demonstrated the use of a multisource single-tree inventory (MS-STI). The two major bottlenecks in the current airborne laser scanning (ALS)-based single-tree-level inventory, tree detection and tree species recognition, are avoided in MS-STI. In addition to airborne 3D data, such as ALS, MS-STI requires an existing tree map with tree species information as the input information. In operational forest management, tree mapping would be carried out after or during the first thinning. It should be highlighted that the tree map is a challenging prerequisite, but that the recent development in mobile 2D and 3D laser scanning indicates that the solution is within reach. In our study, the tested input tree map was produced by terrestrial laser scanning (TLS) and by using a Global Navigation Satellite System. Predictors for tree quality OPEN ACCESSRemote Sens. 2014, 6 3476 attributes were extracted from ALS data or digital stereo imagery (DSI) and used in the nearest-neighbor estimation approach. Stem distribution was compiled by summing the predicted single-tree measures. The accuracy of the MS-STI was validated using harvester data (timber assortments) and field measures (stem diameter, tree height). RMSEs for tree height, diameter, saw log volume and pulpwood volume varied from 4.2% to 5.3%, from 10.9% to 19.9%, from 28.7% to 43.5% and from 125.1% to 134.3%, respectively. Stand-level saw log recoveries differed from −2.2% to 1.3% from the harvester measurements, as the respective differences in pulpwood recovery were between −3.0% and 10.6%. We conclude that MS-STI improves the predictions of stem-diameter distributions and provides accurate estimates for tree quality variables if an accurate tree map is available.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Multisource Single-Tree Inventory in the Prediction of Tree Quality Variables and Logging Recoveries

et al. 2014

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“…Most studies investigating SLAM are mainly for indoor environments, where GNSS is denied and which are full of features. Similarly, the application of SLAM to forests has been investigated in several studies [16][17][18][19][20][21][22][23][24]. In addition, many of them have studied the feasibility of using SLAM technology for MLS in forests [17][18][19].…”

Section: Introductionmentioning

confidence: 99%

An Integrated GNSS/INS/LiDAR-SLAM Positioning Method for Highly Accurate Forest Stem Mapping

et al. 2016

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Forest mapping, one of the main components of performing a forest inventory, is an important driving force in the development of laser scanning. Mobile laser scanning (MLS), in which laser scanners are installed on moving platforms, has been studied as a convenient measurement method for forest mapping in the past several years. Positioning and attitude accuracies are important for forest mapping using MLS systems. Inertial Navigation Systems (INSs) and Global Navigation Satellite Systems (GNSSs) are typical and popular positioning and attitude sensors used in MLS systems. In forest environments, because of the loss of signal due to occlusion and severe multipath effects, the positioning accuracy of GNSS is severely degraded, and even that of GNSS/INS decreases considerably. Light Detection and Ranging (LiDAR)-based Simultaneous Localization and Mapping (SLAM) can achieve higher positioning accuracy in environments containing many features and is commonly implemented in GNSS-denied indoor environments. Forests are different from an indoor environment in that the GNSS signal is available to some extent in a forest. Although the positioning accuracy of GNSS/INS is reduced, estimates of heading angle and velocity can maintain high accurate even with fewer satellites. GNSS/INS and the LiDAR-based SLAM technique can be effectively integrated to form a sustainable, highly accurate positioning and mapping solution for use in forests without additional hardware costs. In this study, information such as heading angles and velocities extracted from a GNSS/INS is utilized to improve the positioning accuracy of the SLAM solution, and two information-aided SLAM methods are proposed. First, a heading angle-aided SLAM (H-aided SLAM) method is proposed that supplies the heading angle from GNSS/INS to SLAM. Field test results show that the horizontal positioning accuracy of an entire trajectory of 800 m is 0.13 m and is significantly improved (by 70%) compared to that of a traditional GNSS/INS; second, a more complex information added SLAM solution that utilizes both heading angle and velocity information simultaneously (HV-aided SLAM) is investigated. Experimental results show that the horizontal positioning accuracy can reach a level of six centimetres with the HV-aided SLAM, which is a significant improvement (by 86%). Thus, a more accurate forest map is obtained by the proposed integrated method.

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Forestry crane posture estimation with a two‐dimensional laser scanner

Hyyti

Lehtola

Visala

2018

Journal of Field Robotics

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Crane posture estimation is the stepping stone to forest machine automation. Here, we introduce a robust minimal perception solution, that is, one that uses minimal constraints for maximal benefits. Specifically, we introduce a robust particle‐filter‐based method to estimate and track the posture of a flexible hydraulic crane by using only low‐cost equipment, namely, a two‐dimensional (2D) laser scanner, two short magnetically attached metal tubes as targets, and an angle sensor. An important feature of our method is that it incorporates control signals for hydraulic actuators. In contrast to the previous works employing laser scanners, we do not use the full shape of the crane to estimate the crane posture, but, instead, we use only two small targets in the field of view of the laser scanner. Thus, a large share of the range data is useful for other purposes, for example, to map the surrounding environment. We test the proposed method in a challenging forest environment and show that the particle filter is able to estimate the posture of the hydraulic crane efficiently and reliably in the presence of occlusions and obstructions. During our comprehensive testing, the tip position was measured with average errors smaller than 4.3 cm whereas the absolute maximum error was 15 cm.

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Enhanced Algorithms for Estimating Tree Trunk Diameter Using 2D Laser Scanner

Cited by 24 publications

References 26 publications

Multisource Single-Tree Inventory in the Prediction of Tree Quality Variables and Logging Recoveries

Multisource Single-Tree Inventory in the Prediction of Tree Quality Variables and Logging Recoveries

An Integrated GNSS/INS/LiDAR-SLAM Positioning Method for Highly Accurate Forest Stem Mapping

Forestry crane posture estimation with a two‐dimensional laser scanner

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