SLOAM: Semantic Lidar Odometry and Mapping for Forest Inventory

Chen, Steven W.; Nardari, Guilherme V.; Lee, Elijah S.; Qu, Chao; Xu, Liu; Romero, Roseli Aparecida Francelin; Kumar, Vijay

doi:10.1109/lra.2019.2963823

Cited by 116 publications

(72 citation statements)

References 28 publications

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“…The researches performed point cloud registration only between the same class from the semantic segmentation. In [ 39 ], Semantic LOAM (SLOAM) was proposed for forest inventory using an end-to-end pipeline for tree diameter estimation. Furthermore, SuMa++ presented semantic ICP and dynamic filtering using the semantic information from RangeNet++ for semantic mapping [ 7 ].…”

Section: Related Workmentioning

confidence: 99%

Semantic Point Cloud Mapping of LiDAR Based on Probabilistic Uncertainty Modeling for Autonomous Driving

Cho

Kim

Park

et al. 2020

Sensors

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LiDAR-based Simultaneous Localization And Mapping (SLAM), which provides environmental information for autonomous vehicles by map building, is a major challenge for autonomous driving. In addition, the semantic information has been used for the LiDAR-based SLAM with the advent of deep neural network-based semantic segmentation algorithms. The semantic segmented point clouds provide a much greater range of functionality for autonomous vehicles than geometry alone, which can play an important role in the mapping step. However, due to the uncertainty of the semantic segmentation algorithms, the semantic segmented point clouds have limitations in being directly used for SLAM. In order to solve the limitations, this paper proposes a semantic segmentation-based LiDAR SLAM system considering the uncertainty of the semantic segmentation algorithms. The uncertainty is explicitly modeled by proposed probability models which are come from the data-driven approaches. Based on the probability models, this paper proposes semantic registration which calculates the transformation relationship of consecutive point clouds using semantic information with proposed probability models. Furthermore, the proposed probability models are used to determine the semantic class of the points when the multiple scans indicate different classes due to the uncertainty. The proposed framework is verified and evaluated by the KITTI dataset and outdoor environments. The experiment results show that the proposed semantic mapping framework reduces the errors of the mapping poses and eliminates the ambiguity of the semantic information of the generated semantic map.

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Section: Related Workmentioning

confidence: 99%

Semantic Point Cloud Mapping of LiDAR Based on Probabilistic Uncertainty Modeling for Autonomous Driving

Cho

Kim

Park

et al. 2020

Sensors

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“…For instance, LeGO-LOAM [11] is a lightweight version of LOAM that filters out noise and takes advantage of the ground plane. Another group of methods focuses on leveraging semantic information about the 3D points, such as SuMa++ [12], which embeds these labels into a surfel-based map representation, or SLOAM [13], oriented towards forest environments.…”

Section: Related Workmentioning

confidence: 99%

Study of the Effect of Exploiting 3D Semantic Segmentation in LiDAR Odometry

et al. 2020

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This paper presents a study of how the performance of LiDAR odometry is affected by the preprocessing of the point cloud through the use of 3D semantic segmentation. The study analyzed the estimated trajectories when the semantic information is exploited to filter the original raw data. Different filtering configurations were tested: raw (original point cloud), dynamic (dynamic obstacles are removed from the point cloud), dynamic vehicles (vehicles are removed), far (distant points are removed), ground (the points belonging to the ground are removed) and structure (only structures and objects are kept in the point cloud). The experiments were performed using the KITTI and SemanticKITTI datasets, which feature different scenarios that allowed identifying the implications and relevance of each element of the environment in LiDAR odometry algorithms. The conclusions obtained from this work are of special relevance for improving the efficiency of LiDAR odometry algorithms in all kinds of scenarios.

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“…Reliable and efficient methods for assessing forests' physical structures are needed for numerous applications in forestry, ecology, and conservation [1][2][3]. The estimation of quantities such as stand volume, biomass, productivity, and basal area all depend on physical measurements of trees.…”

Section: Introductionmentioning

confidence: 99%

“…Satellite platforms can survey much larger areas, but at lower resolution. Recent years have seen major advances in the use of cameras [10,11] and LiDAR [12][13][14] for forest scanning, and in the software used to turn the scans into digital models from which forests' physical structures can be measured [1,3,15]. Most UAV-based surveys of forests to date have used above-canopy UAVs [2,10,12,[16][17][18][19][20][21].…”

Section: Introductionmentioning

confidence: 99%

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Estimating Tree Diameters from an Autonomous Below-Canopy UAV with Mounted LiDAR

2021

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Below-canopy UAVs hold promise for automated forest surveys because their sensors can provide detailed information on below-canopy forest structures, especially in dense forests, which may be inaccessible to above-canopy UAVs, aircraft, and satellites. We present an end-to-end autonomous system for estimating tree diameters using a below-canopy UAV in parklands. We used simultaneous localization and mapping (SLAM) and LiDAR data produced at flight time as inputs to diameter-estimation algorithms in post-processing. The SLAM path was used for initial compilation of horizontal LiDAR scans into a 2D cross-sectional map, and then optimization algorithms aligned the scans for each tree within the 2D map to achieve a precision suitable for diameter measurement. The algorithms successfully identified 12 objects, 11 of which were trees and one a lamppost. For these, the estimated diameters from the autonomous survey were highly correlated with manual ground-truthed diameters (R2=0.92, root mean squared error = 30.6%, bias = 18.4%). Autonomous measurement was most effective for larger trees (>300 mm diameter) within 10 m of the UAV flight path, for medium trees (200–300 mm diameter) within 5 m, and for trees with regular cross sections. We conclude that fully automated below-canopy forest surveys are a promising, but still nascent, technology and suggest directions for future research.

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SLOAM: Semantic Lidar Odometry and Mapping for Forest Inventory

Cited by 116 publications

References 28 publications

Semantic Point Cloud Mapping of LiDAR Based on Probabilistic Uncertainty Modeling for Autonomous Driving

Semantic Point Cloud Mapping of LiDAR Based on Probabilistic Uncertainty Modeling for Autonomous Driving

Study of the Effect of Exploiting 3D Semantic Segmentation in LiDAR Odometry

Estimating Tree Diameters from an Autonomous Below-Canopy UAV with Mounted LiDAR

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