Detecting and Mapping Trees in Unstructured Environments with a Stereo Camera and Pseudo-Lidar

Wang, Brian H.; Diaz-Ruiz, Carlos A.; Banfi, Jacopo; Campbell, Mark

doi:10.1109/icra48506.2021.9562056

Cited by 5 publications

(5 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For all biophysical parameters, the correlation between estimated and ground truth values was very high, as indicated by the values of R 2 and the regression line slope, which are both very close to unity. Although similarly good results have been reported in other studies [57,59,64,67,68,73,84], this study is the first to present an extensive validation and analysis of the use of stereoscopic photogrammetry for tree parameter estimation in a real forest setting. The scattering profiles represent the variability in the individual measurements around the regression line.…”

Section: Discussionsupporting

confidence: 82%

Low-Cost Non-Contact Forest Inventory: A Case Study of Kieni Forest in Kenya

Kiplimo,

wa Maina,

Okal

2024

Challenges

View full text Add to dashboard Cite

Forests are a vital source of food, fuel, and medicine and play a crucial role in climate change mitigation. Strategic and policy decisions on forest management and conservation require accurate and up-to-date information on available forest resources. Forest inventory data such as tree parameters, heights, and crown diameters must be collected and analysed to monitor forests effectively. Traditional manual techniques are slow and labour-intensive, requiring additional personnel, while existing non-contact methods are costly, computationally intensive, or less accurate. Kenya plans to increase its forest cover to 30% by 2032 and establish a national forest monitoring system. Building capacity in forest monitoring through innovative field data collection technologies is encouraged to match the pace of increase in forest cover. This study explored the applicability of low-cost, non-contact tree inventory based on stereoscopic photogrammetry in a recently reforested stand in Kieni Forest, Kenya. A custom-built stereo camera was used to capture images of 251 trees in the study area from which the tree heights and crown diameters were successfully extracted quickly and with high accuracy. The results imply that stereoscopic photogrammetry is an accurate and reliable method that can support the national forest monitoring system and REDD+ implementation.

show abstract

Section: Discussionsupporting

confidence: 82%

Low-Cost Non-Contact Forest Inventory: A Case Study of Kieni Forest in Kenya

Kiplimo,

wa Maina,

Okal

2024

Challenges

View full text Add to dashboard Cite

show abstract

“…On the other hand, our system only needs one OAK-D to give inertial data, coloured and depth image data, and also object detection data, that are fed, in real-time, to higher-level algorithms. Another work that aimed at mapping trees was the one presented in [27], where the authors also used a ZED2 stereo camera but instead of training DL algorithms to detect object in images (in 2D), they trained a 3D object detector to detect tree trunks in 3D data provided by the stereo camera. Then, they used the spatial mapping programming interface of the manufacturers of the stereo camera to map the detected trees in 3D space, and after, they applied a clustering method to extract only the trees from the 3D map.…”

Section: Discussionmentioning

confidence: 99%

“…By means of a structured light camera, the authors were capable of extracting 3D information about the logs and, after some 3D segmentation steps and an extrinsic calibration, they reconstructed the logs and estimated their respective poses. The authors concluded that the reconstruction errors increased exponentially relatively to the distance to the logs, and that best results were obtained under 3 m. Another work where the authors proposed the 3D detection of tree trunks by means of cameras (in this case a stereo-pair) was presented in [27]. Here, a DL-based 3D object detector was trained on point clouds of tree trunks acquired with a ZED Stereo 2 camera.…”

Section: Introductionmentioning

confidence: 99%

Edge AI-Based Tree Trunk Detection for Forestry Monitoring Robotics

et al. 2022

View full text Add to dashboard Cite

Object identification, such as tree trunk detection, is fundamental for forest robotics. Intelligent vision systems are of paramount importance in order to improve robotic perception, thus enhancing the autonomy of forest robots. To that purpose, this paper presents three contributions: an open dataset of 5325 annotated forest images; a tree trunk detection Edge AI benchmark between 13 deep learning models evaluated on four edge-devices (CPU, TPU, GPU and VPU); and a tree trunk mapping experiment using an OAK-D as a sensing device. The results showed that YOLOR was the most reliable trunk detector, achieving a maximum F1 score around 90% while maintaining high scores for different confidence levels; in terms of inference time, YOLOv4 Tiny was the fastest model, attaining 1.93 ms on the GPU. YOLOv7 Tiny presented the best trade-off between detection accuracy and speed, with average inference times under 4 ms on the GPU considering different input resolutions and at the same time achieving an F1 score similar to YOLOR. This work will enable the development of advanced artificial vision systems for robotics in forestry monitoring operations.

show abstract

“…Wang et al (2021) used a point RCNN detector to identify trees in a forest‐like environment. The algorithm relies on an automatic labelling process to generate obstacle detector training data and cluster the fused global point cloud.…”

Section: Path Planning For Uav Inspectionmentioning

confidence: 99%

Unmanned aerial vehicle navigation in underground structure inspection: A review

Zhang,

Hao,

Zhang

et al. 2023

Geological Journal

View full text Add to dashboard Cite

Many years after construction, a number of existing old tunnels and underground structures are deteriorating with time as evidenced by cracks, large deformations, water leakage and so forth, which usually require regular site inspections to record their structural deterioration by taking high‐pixel, high‐overlap images along miles of a tunnel network. For complex underground structures (e.g., long tunnels and large caves), unmanned aerial vehicles (UAVs) may be adaptive in acquiring images at multiple heights and angles with low operational costs. So far, UAV underground structural health monitoring has become mature for open‐air surveying with rapid developments in robotic software and hardware. However, the UAV image acquisition for underground working conditions still faces a number of key challenges. This paper aims to provide an overview of UAV navigation techniques in confined dark spaces for geotechnical engineers, geologists, drone developers and other interdisciplinary researchers & professionals in the structural health monitoring field. It specifies the challenges for UAV application in underground space, mainly including lack of Global Navigation Satellite System (GNSS) signals, poor lighting conditions, weak features and obstacle avoidance and then followed by strategic solutions. For example, in light of poor GNSS signals, the fusion of multi‐sensors (e.g., laser imaging, detection and ranging (LiDAR) and multi‐cameras) can enhance localization accuracy in low‐luminance underground conditions. To address obstacle avoidance, computer vision (CV)‐based navigation algorithms (e.g., deep reinforced learning [DRL]) enable effective navigation in complex 3D spaces, but their adaptability is limited by arithmetic power and pre‐training needs. The review of relevant previous studies concludes that further development for UAVs in underground space inspection may focus on operation in large‐scale geometric inspection environments, obstacle avoidance, features and semantic recognition.

show abstract

Detecting and Mapping Trees in Unstructured Environments with a Stereo Camera and Pseudo-Lidar

Cited by 5 publications

References 17 publications

Low-Cost Non-Contact Forest Inventory: A Case Study of Kieni Forest in Kenya

Low-Cost Non-Contact Forest Inventory: A Case Study of Kieni Forest in Kenya

Edge AI-Based Tree Trunk Detection for Forestry Monitoring Robotics

Unmanned aerial vehicle navigation in underground structure inspection: A review

Contact Info

Product

Resources

About