Extending Deep Neural Network Trail Navigation for Unmanned Aerial Vehicle Operation Within the Forest Canopy

Maciel-Pearson, Bruna G.; Carbonneau, Patrice; Breckon, Toby P.

doi:10.1007/978-3-319-96728-8_13

Cited by 13 publications

(11 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The system automation is complex and challenging, as a UAV has six dimensions of freedom in motion, i.e., the translations and rotations with respect to the X, Y, and Z axis. Several recent studies touched the problem of autonomous drone navigation inside the forest, e.g., (Chisholm et al 2013;Dionisio-Ortega et al 2018;Maciel-Pearson et al 2018), and many other studies of drone navigation in other GNSS-denied environments are also inspiring (Prakash et al 2019;Wang et al 2019). Nevertheless, a fully automated operation of an inside canopy ULS is still at a preliminary stage.…”

Section: Discussionmentioning

confidence: 99%

Seamless integration of above- and under-canopy unmanned aerial vehicle laser scanning for forest investigation

et al. 2021

View full text Add to dashboard Cite

Background Current automated forest investigation is facing a dilemma over how to achieve high tree- and plot-level completeness while maintaining a high cost and labor efficiency. This study tackles the challenge by exploring a new concept that enables an efficient fusion of aerial and terrestrial perspectives for digitizing and characterizing individual trees in forests through an Unmanned Aerial Vehicle (UAV) that flies above and under canopies in a single operation. The advantage of such concept is that the aerial perspective from the above-canopy UAV and the terrestrial perspective from the under-canopy UAV can be seamlessly integrated in one flight, thus grants the access to simultaneous high completeness, high efficiency, and low cost. Results In the experiment, an approximately 0.5 ha forest was covered in ca. 10 min from takeoff to landing. The GNSS-IMU based positioning supports a geometric accuracy of the produced point cloud that is equivalent to that of the mobile mapping systems, which leads to a 2–4 cm RMSE of the diameter at the breast height estimates, and a 4–7 cm RMSE of the stem curve estimates. Conclusions Results of the experiment suggested that the integrated flight is capable of combining the high completeness of upper canopies from the above-canopy perspective and the high completeness of stems from the terrestrial perspective. Thus, it is a solution to combine the advantages of the terrestrial static, the mobile, and the above-canopy UAV observations, which is a promising step forward to achieve a fully autonomous in situ forest inventory. Future studies should be aimed to further improve the platform positioning, and to automatize the UAV operation.

show abstract

Section: Discussionmentioning

confidence: 99%

Seamless integration of above- and under-canopy unmanned aerial vehicle laser scanning for forest investigation

et al. 2021

View full text Add to dashboard Cite

show abstract

“…Many works employ UAVs in forests [8]- [14]. Most enable autonomous navigation by detecting and following forest trails using learning-based approaches [9], [11], [12]. Cui et al [8] uses a 2D laser range finder to navigate autonomously, but makes the assumption that the UAV height does not drastically change.…”

Section: Introductionmentioning

confidence: 99%

SLOAM: Semantic Lidar Odometry and Mapping for Forest Inventory

Chen

Nardari

Lee

et al. 2020

IEEE Robot. Autom. Lett.

143

View full text Add to dashboard Cite

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.

show abstract

“…A number of recent works have looked at the problem of visual trail following under forest canopy, starting with Giusti et al ( 2015 ) and more recently Maciel-Pearson et al ( 2018 ). These represent only reactive navigation, rather than mapping, but what they do show us is the potential of neural network based approaches for cutting through the visual complexity of forest scenes.…”

Section: Related Workmentioning

confidence: 99%

Lost in the Woods? Place Recognition for Navigation in Difficult Forest Environments

Garforth

Webb

2020

Front. Robot. AI

View full text Add to dashboard Cite

Forests present one of the most challenging environments for computer vision due to traits, such as complex texture, rapidly changing lighting, and high dynamicity. Loop closure by place recognition is a crucial part of successfully deploying robotic systems to map forests for the purpose of automating conservation. Modern CNN-based place recognition systems like NetVLAD have reported promising results, but the datasets used to train and test them are primarily of urban scenes. In this paper, we investigate how well NetVLAD generalizes to forest environments and find that it out performs state of the art loop closure approaches. Finally, integrating NetVLAD with ORBSLAM2 and evaluating on a novel forest data set, we find that, although suitable locations for loop closure can be identified, the SLAM system is unable to resolve matched places with feature correspondences. We discuss additional considerations to be addressed in future to deal with this challenging problem.

show abstract

Extending Deep Neural Network Trail Navigation for Unmanned Aerial Vehicle Operation Within the Forest Canopy

Cited by 13 publications

References 11 publications

Seamless integration of above- and under-canopy unmanned aerial vehicle laser scanning for forest investigation

Seamless integration of above- and under-canopy unmanned aerial vehicle laser scanning for forest investigation

SLOAM: Semantic Lidar Odometry and Mapping for Forest Inventory

Lost in the Woods? Place Recognition for Navigation in Difficult Forest Environments

Contact Info

Product

Resources

About