An adaptive compensation strategy for sensors based on the degree of degradation

Abstract

Simultaneous Localization and Mapping (SLAM) is widely used to solve the localization problem of unmanned devices such as robots. However, in degraded environments, the accuracy of SLAM is greatly reduced due to the lack of constrained features. In this article, we propose a deep learning-based adaptive compensation strategy for sensors. First, we create a dataset dedicated to training a degradation detection model, which contains coordinate data of particle swarms with different distributional features, and endow the model with degradation detection capability through supervised learning. Second, we design a lightweight network model with short computation time and good accuracy for real-time degradation detection tasks. Finally, an adaptive compensation strategy for sensors based on the degree of degradation is designed, where the SLAM is able to assign different weights to the sensor information according to the degree of degradation given by the model, to adjust the contribution of different sensors in the pose optimization process. We demonstrate through simulation experiments and real experiments that the robustness of the improved SLAM in degraded environments is significantly enhanced, and the accuracy of localization and mapping are improved.