With the booming development of computer, optical and sensing technologies and cybernetics, the technical research in unmanned vehicle has been advanced to a new era. This trend arouses great interest in simultaneous localization and mapping (SLAM). Especially, light detection and ranging (Lidar)-based SLAM system has the characteristics of high measuring accuracy and insensitivity to illumination conditions, which has been widely used in industry. However, SLAM has some intractable problems, including degradation under less structured or uncontrived environment. To solve this problem, this paper aims to propose an adaptive scheme with dynamic threshold to mitigate degradation.
We propose an adaptive strategy with a dynamic module is proposed to overcome degradation of point cloud. Besides, a distortion correction process is presented in the local map to reduce the impact of noise in the iterative optimization process. Our solution ensures adaptability to environmental changes.
Experimental results on both public data set and field tests demonstrated that the algorithm is robust and self-adaptive, which achieved higher localization accuracy and lower mapping error compared with existing methods.
Unlike other popular algorithms, we do not rely on multi-sensor fusion to improve the localization accuracy. Instead, the pure Lidar-based method with dynamic threshold and distortion correction module indeed improved the accuracy and robustness in localization results.
