This invention discloses a rapid mapping, localization, and statistical method for roadside trees based on handheld SLAM, including
data acquisition, target extraction, and localization and statistics. Compared with traditional tree-by-
tree measurement, it enables
continuous data acquisition while walking, obtaining three-dimensional information of all roadside trees in one go, improving
field data acquisition efficiency by 80%. Through dynamic interference filtering and roadside tree feature enhancement, it reduces the
impact of dynamic objects such as vehicles and pedestrians on
point cloud quality, adapts to complex street scenes, and improves the stability and accuracy of modeling in urban environments. Utilizing the linear arrangement and morphological characteristics of roadside trees, it effectively distinguishes trees from similar features such as streetlights and traffic signs, achieving high recognition accuracy and reducing misidentification and missed identification.
Cloud processing is integrated with
greening maintenance operations, automatically extracting multi-dimensional parameters such as tree height,
diameter at breast height (DBH), and crown width while acquiring the location of individual trees, enabling business-oriented
data processing and providing refined data support for
greening management, tree health monitoring, and green volume calculation.