Autonomous navigation of unmanned vehicles in forests is a challenging task. In such environments, due to the canopies of the trees, information from Global Navigation Satellite Systems (GNSS) can be degraded or even unavailable. Also, because of the large number of obstacles, a previous detailed map of the environment is not practical. In this paper, we solve the complete navigation problem of an aerial robot in a sparse forest, where there is enough space for the flight and the GNSS signals can be sporadically detected. For localization, we propose a state estimator that merges information from GNSS, Attitude and Heading Reference Systems (AHRS), and odometry based on Light Detection and Ranging (LiDAR) sensors. In our LiDAR-based odometry solution, the trunks of the trees are used in a feature-based scan matching algorithm to estimate the relative movement of the vehicle. Our method employs a robust adaptive fusion algorithm based on the unscented Kalman filter. For motion control, we adopt a strategy that integrates a vector field, used to impose the main direction of the movement for the robot, with an optimal probabilistic planner, which is responsible for obstacle avoidance. Experiments with a quadrotor equipped with a planar LiDAR in an actual forest environment is used to illustrate the effectiveness of our approach.

https://researchrepository.wvu.edu/cgi/viewcontent.cgi?article=2615&context=faculty_publications

GNSS/LiDAR-Based Navigation of an Aerial Robot in Sparse Forests

FPGA Implementation of a Binarized Dual Stream Convolutional Neural Network for Service Robots

Unmanned Aerial Systems (UAS) are a cost-effective means of collecting forest data conventionally used above the forest canopy. Where forest canopies are dense, limited information about stem structures can be extracted directly due to obscuration by foliage. In these circumstances, complementary ground-based methods including manual measurement and terrestrial laser scanning are deployed, but these techniques are often limited in terms of the scope and scale of data collected by factors including time, field cost and site accessibility. This paper describes the application of a UAS flown below the forest canopy as an efficient and effective approach for stem measurement in areas where the canopy is difficult to penetrate, and as a potential solution to measuring trees in areas of dense undergrowth. The study sites were scanned with a helicopter-mounted VUX-1LR LiDAR sensor and the resulting point clouds were used as a comparison dataset. The measurements extracted from these point-clouds were compared with ground-based measurements of diameter at breast height and relative positions. The below-canopy UAS and the VUX-1LR at 30m had the lowest root-mean-squarederror (RMSE) of 4.1cm, followed by the VUX-1LR at 90m with a RMSE of 4.4cm. The VUX-1LR 60m flight was the most consistent with the highest coefficient of determination, however due to a positive bias, there was an RMSE of 4.5cm. The photogrammetry-based, below-canopy UAS was found to be an efficient and accurate method of extracting DBH and relative position of stems in forests.

Below-canopy UAS photogrammetry for stem measurement in radiata pine plantation

This paper presents an effective mobile three-dimensional (3D) measurement system that can obtain measurements from the inside of large structures such as railway vehicles, elevators, and escalators. In the proposed method, images are acquired by moving measurement equipment composed of a ring laser and two cameras. From the acquired images, accurate cross-sectional shapes, which are obtained via a light-section method by each camera, are integrated into a unified coordinate system using pose estimation based on bundle adjustment. We focus on the method of separately extracting the information necessary for the two processes – the light-section method and pose estimation – from the acquired images. The laser areas used for the light-section method are detected by a bandpass color filter. Further, a new block matching technique is introduced to eliminate the influence of the laser light, which causes incorrect detection of corresponding points. Through an experiment, we confirm the validity of the proposed 3D measurement method.

3D Measurement of Large Structure by Multiple Cameras and a Ring Laser

This paper presents an integrated system for performing precision harvesting missions using a legged harvester. Our harvester performs a challenging task of autonomous navigation and tree grabbing in a confined, GPS denied forest environment. Strategies for mapping, localization, planning, and control are proposed and integrated into a fully autonomous system. The mission starts with a human mapping the area of interest using a custom-made sensor module. Subsequently, a human expert selects the trees for harvesting. The sensor module is then mounted on the machine and used for localization within the given map. A planning algorithm searches for both an approach pose and a path in a single path planning problem. We design a path following controller leveraging the legged harvester's capabilities for negotiating rough terrain. Upon reaching the approach pose, the machine grabs a tree with a general-purpose gripper. This process repeats for all the trees selected by the operator. Our system has been tested on a testing field with tree trunks and in a natural forest. To the best of our knowledge, this is the first time this level of autonomy has been shown on a full-size hydraulic machine operating in a realistic environment.

Towards Autonomous Robotic Precision Harvesting: Mapping, Localization, Planning and Control for a Legged Tree Harvester

Development of Robot for 3D Measurement of Forest Environment

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Development of Robot for 3D Measurement of Forest Environment