SLOAM: Semantic Lidar Odometry and Mapping for Forest Inventory

• URL: http://arxiv.org/abs/1912.12726v1
• Date: Sun, 29 Dec 2019 20:38:32 GMT
• Title: SLOAM: Semantic Lidar Odometry and Mapping for Forest Inventory
• Authors: Steven W. Chen, Guilherme V. Nardari, Elijah S. Lee, Chao Qu, Xu Liu, Roseli A. F. Romero, Vijay Kumar
• Abstract summary: This paper describes an end-to-end pipeline for tree diameter estimation based on semantic segmentation and lidar odometry and mapping. We propose a semantic feature based pose optimization that simultaneously refines the tree models while estimating the robot pose. We show that traditional lidar and image based methods fail in the forest environment on both Unmanned Aerial Vehicle (UAV) and hand-carry systems.
• Score: 9.927061361867237
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper describes an end-to-end pipeline for tree diameter estimation based on semantic segmentation and lidar odometry and mapping. Accurate mapping of this type of environment is challenging since the ground and the trees are surrounded by leaves, thorns and vines, and the sensor typically experiences extreme motion. We propose a semantic feature based pose optimization that simultaneously refines the tree models while estimating the robot pose. The pipeline utilizes a custom virtual reality tool for labeling 3D scans that is used to train a semantic segmentation network. The masked point cloud is used to compute a trellis graph that identifies individual instances and extracts relevant features that are used by the SLAM module. We show that traditional lidar and image based methods fail in the forest environment on both Unmanned Aerial Vehicle (UAV) and hand-carry systems, while our method is more robust, scalable, and automatically generates tree diameter estimations.

Related papers

• Open-Vocabulary Octree-Graph for 3D Scene Understanding [54.11828083068082]
  Octree-Graph is a novel scene representation for open-vocabulary 3D scene understanding. An adaptive-octree structure is developed that stores semantics and depicts the occupancy of an object adjustably according to its shape.
  arXiv  Detail & Related papers  (2024-11-25T10:14:10Z)
• NeRF-Accelerated Ecological Monitoring in Mixed-Evergreen Redwood Forest [0.0]
  We present a comparison of MLS and NeRF forest reconstructions for the purpose of trunk diameter estimation in a mixed-evergreen Redwood forest. We propose an improved DBH-estimation method using convex-hull modeling.
  arXiv  Detail & Related papers  (2024-10-09T20:32:15Z)
• ViTree: Single-path Neural Tree for Step-wise Interpretable Fine-grained Visual Categorization [56.37520969273242]
  We introduce ViTree, a novel approach for fine-grained visual categorization. By traversing the tree paths, ViTree effectively selects patches from transformer-processed features to highlight informative local regions. This patch and path selectivity enhances model interpretability of ViTree, enabling better insights into the model's inner workings.
  arXiv  Detail & Related papers  (2024-01-30T14:32:25Z)
• Benchmarking Individual Tree Mapping with Sub-meter Imagery [6.907098367807166]
  We introduce an evaluation framework suited for individual tree mapping in any physical environment. We review and compare different approaches and deep architectures, and introduce a new method that we experimentally prove to be a good compromise between segmentation and detection.
  arXiv  Detail & Related papers  (2023-11-14T08:21:36Z)
• Neural Semantic Surface Maps [52.61017226479506]
  We present an automated technique for computing a map between two genus-zero shapes, which matches semantically corresponding regions to one another. Our approach can generate semantic surface-to-surface maps, eliminating manual annotations or any 3D training data requirement.
  arXiv  Detail & Related papers  (2023-09-09T16:21:56Z)
• CherryPicker: Semantic Skeletonization and Topological Reconstruction of Cherry Trees [3.8697834534260447]
  We present CherryPicker, an automatic pipeline that reconstructs photo-metric point clouds of trees. Our system combines several state-of-the-art algorithms to enable automatic processing for further usage in 3D-plant phenotyping applications.
  arXiv  Detail & Related papers  (2023-04-10T16:54:05Z)
• ALSO: Automotive Lidar Self-supervision by Occupancy estimation [70.70557577874155]
  We propose a new self-supervised method for pre-training the backbone of deep perception models operating on point clouds. The core idea is to train the model on a pretext task which is the reconstruction of the surface on which the 3D points are sampled. The intuition is that if the network is able to reconstruct the scene surface, given only sparse input points, then it probably also captures some fragments of semantic information.
  arXiv  Detail & Related papers  (2022-12-12T13:10:19Z)
• Tree Detection and Diameter Estimation Based on Deep Learning [0.0]
  Tree perception is an essential building block toward autonomous forestry operations. Deep neural network models trained on our datasets achieve a precision of 90.4% for tree detection. Results offer promising avenues toward autonomous tree felling operations.
  arXiv  Detail & Related papers  (2022-10-31T15:51:32Z)
• TANDEM: Tracking and Dense Mapping in Real-time using Deep Multi-view Stereo [55.30992853477754]
  We present TANDEM, a real-time monocular tracking and dense framework. For pose estimation, TANDEM performs photometric bundle adjustment based on a sliding window of alignments. TANDEM shows state-of-the-art real-time 3D reconstruction performance.
  arXiv  Detail & Related papers  (2021-11-14T19:01:02Z)
• Large-scale Autonomous Flight with Real-time Semantic SLAM under Dense Forest Canopy [48.51396198176273]
  We propose an integrated system that can perform large-scale autonomous flights and real-time semantic mapping in challenging under-canopy environments. We detect and model tree trunks and ground planes from LiDAR data, which are associated across scans and used to constrain robot poses as well as tree trunk models. A drift-compensation mechanism is designed to minimize the odometry drift using semantic SLAM outputs in real time, while maintaining planner optimality and controller stability.
  arXiv  Detail & Related papers  (2021-09-14T07:24:53Z)
• SimTreeLS: Simulating aerial and terrestrial laser scans of trees [0.0]
  We present an open source tool, SimTreeLS, for generating point clouds which simulate scanning with user-defined sensor, trajectory, tree shape and layout parameters. Material classification is kept in a pointwise fashion so leaf and woody matter are perfectly known. SimTreeLS is available as an open source resource built on publicly available libraries.
  arXiv  Detail & Related papers  (2020-11-24T08:25:42Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.