Objective Forestry survey planning is important for the sustainable development of forestry. When using mobile lidar to do forest sample mapping and measurement, the accuracy of the global consistency map is closely related to the scanning trajectory. Therefore, the rational planning of the sample observation path is of great significance.

Method In this study, the forest sample was scanned by the handheld lidar with the use of simultaneous localization and mapping (SLAM). Based on the researches in the application of SLAM technology and the characteristics of forest sample land, three handheld mobile lidar scanning path plans in the sample site were proposed. And the point cloud map was constructed through LEGO LOAM algorithm. Thus, the three paths were compared and analyzed in terms of the mapping effects, the accuracy of the diameter of the standing wood chest and the accuracy of its position (curve fitting data).

Result The estimated diameter of the prototype stumpage chest diameter fitted by the progressive path 1 has a deviation of 2.18 cm, a relative deviation of 7.74%, and a Root mean square error (RMSE) of 2.74 cm, and its accuracy is higher than the other two paths; path 1 fits the rib diameter of the substation boundary standing wood and the internal standing wood better than path 2 and path 3; in terms of the standing wood position fitting accuracy, path 1 and path 3 have a better overall fit effect, and the accuracy of path 1 is slightly better than path 3, with an x-axis estimated root mean square error (RMSE) of 0.077 m, a y-axis estimation root mean square error of 0.157 m, and a covariance difference of the maximum error direction of 0.124 m, all of which are smaller than the other two paths’.

Conclusion When collecting data through 32-line lidar, with the use of point cloud mapping of the forest-like land and the LEGO LOAM algorithm, to achieve the fit measurement of the single wood factor, the overall mapping effect and the measurement accuracy of the aerial photogrammetry route like path plan (Path 1) are relatively better than the other paths’. Based on this path plan, the mapping and measurement accuracy can be further improved by increasing the ground identification point and increasing the distance between the scanning path boundary and the sample point boundary. This can provide a reasonable path plan design for ground mobile lidar field data acquisition.