Benchmarking of Laser-Based Simultaneous Localization and Mapping Methods in Forest Environments

Simultaneous localization and mapping (SLAM) based on laser scanning (LS) has been quickly developed in the last decades. However, the application of LS-SLAM in forest environments is still at an early development stage, limited by the challenges posed by forest environments, such as geometric degeneration and global navigation satellite system (GNSS) denied. The applicability, strengths, and weaknesses of the state-of-the-art LS-SLAM methods has not been investigated and even not been assessed. This study quantitatively evaluated nine state-of-the-art LS-SLAM methods in 12 subtropical forest plots with different levels of complexity, that is, “easy,” “medium,” and “difficult.” In addition, a robust 3-D LS-SLAM method specially designed for real-time forest mapping is proposed. This solution extracts angle-based features and applies a continuous filter in 2-D angle image space to identify stable features for enhancing the data alignment performance. The benchmarking results indicated that 1) the light detection and ranging (LiDAR)-only SLAM methods presented an average trajectory accuracy at the 15 and 25 cm levels in easy and medium plots, respectively, and failed in difficult plots; 2) the LiDAR-inertial measurement unit (IMU) SLAM presented an equivalent or better accuracy in comparison with the LiDAR-only methods, yet their performance was still limited in difficult plots; 3) the SLAM methods with back-end optimization significantly improved the localization results, that is, with the 10 cm level accuracy in the easy and medium plots and succeeded in all difficult plots; 4) the trajectory accuracy of the proposed method was at the 5 cm level in all complexity categories, and compared to the state-of-the-art SLAM methods, the trajectory estimation accuracy has doubled, with the root mean squared error (RMSE) decreasing from the 10 to 5 cm level; and 5) the IMU data, properly designed feature extraction (FE) method, loop closure, and back-end optimization modules play a crucial role in resolving aggressive motion and enhancing the alignment accuracy and robustness. These outcomes provide valuable guidelines for researchers and practitioners to select existing SLAM solutions in different forest conditions and applications, while opening a discussion on the future developments that are still necessary.