Detection and Modeling of Unstructured Roads in Forest Areas Based on Visual-2D Lidar Data Fusion

Abstract

The detection and recognition of unstructured roads in forest environments are critical for smart forestry technology. Forest roads lack effective reference objects and manual signs and have high degrees of nonlinearity and uncertainty, which pose severe challenges to forest engineering vehicles. This research aims to improve the automation and intelligence of forestry engineering and proposes an unstructured road detection and recognition method based on a combination of image processing and 2D lidar detection. This method uses the “improved SEEDS + Support Vector Machine (SVM)” strategy to quickly classify and recognize the road area in the image. Combined with the remapping of 2D lidar point cloud data on the image, the actual navigation requirements of forest unmanned navigation vehicles were fully considered, and road model construction based on the vehicle coordinate system was achieved. The algorithm was transplanted to a self-built intelligent navigation platform to verify its feasibility and effectiveness. The experimental results show that under low-speed conditions, the system can meet the real-time requirements of processing data at an average of 10 frames/s. For the centerline of the road model, the matching error between the image and lidar is no more than 0.119 m. The algorithm can provide effective support for the identification of unstructured roads in forest areas. This technology has important application value for forestry engineering vehicles in autonomous inspection and spraying, nursery stock harvesting, skidding, and transportation.

Robust Scan Registration for Navigation in Forest Environment Using Low-Resolution LiDAR Sensors

“…Additionally, forest road maps are often less precise than other scenarios, while autonomous vehicles are often required to leave the mapped road to perform operations [ 13 ]. As a result, navigation of forestry machines relies most often on the fusion of range sensors [ 13 , 15 , 16 , 17 ].…”

Unimodal and Multimodal Perception for Forest Management: Review and Dataset

“…The detection of occluded obstacles in forests is a major issue, as it is important that mobile platforms avoid crashing into other objects while traversing the forest. With this in mind, the authors in [88] were capable of detecting a tree trunk behind a maximum foliage thickness of 2.5 m. Some works related to detection in forests are focused on detecting terrain surfaces by means of LiDAR and vision-based data [89], while others, that also used LiDAR data combined with images, are focused on detecting roads instead [90].…”

A hot-spot analysis of forest roads based on soil erosion and sediment in Zagros forest lands, Iran

“…In recent years, the activities of construction, repair, and maintenance of forest roads have received more attention than before because the construction of road networks increases concerns about short-term and long-term effects on the environment and destruction (Lei et al 2021;Misirlioğlu et al 2022). Therefore, in the design of the road network, not only the amount of costs but also proper management of water and soil should be considered.…”