Forest inventory holds an essential role in forest management and research, but the existing field inventory methods are highly time-consuming and labor-intensive. Here, we developed a simultaneous localization and mapping-based backpack light detection and ranging (LiDAR) system with dual orthogonal laser scanners and an open-source Python package called Forest3D for efficient and accurate forest inventory applications. Two key forest inventory variables, tree height and diameter at breast height (DBH), were extracted at six study sites with different tree species compositions. In addition, the vertical point density distribution and leaf area density (LAD) were calculated for two complex natural forest sites. The results showed that the backpack LiDAR system together with the Forest3D package accurately estimated the tree height (<inline-formula> <tex-math notation="LaTeX">$R^{2} = 0.65$ </tex-math></inline-formula>, RMSE = 1.90 m) and DBH (<inline-formula> <tex-math notation="LaTeX">$R^{2} = 0.95$ </tex-math></inline-formula>, RMSE = 0.02 m), which were equivalent to those derived from terrestrial laser scanning (TLS), but with much higher efficiency. The point density of the backpack LiDAR data was higher than or the same as that of the TLS data across all height strata, and the estimated LAD fit well with the TLS estimates (<inline-formula> <tex-math notation="LaTeX">$R^{2} > 0.92$ </tex-math></inline-formula>, RMSE = 0.01 m<sup>2</sup>/m<sup>3</sup>). The backpack LiDAR system, along with the Forest3D package, provides an efficient and accurate solution for extracting forest inventory variables, which should be of great interests to forest managers and researchers.