Abstract
The Simultaneous Localization and Mapping (SLAM) method of mobile robots has the problem of low accuracy in complex environments with dense clutter and various map features, such as complex indoor environments and underwater environments. This problem is mainly embodied in estimating the location and number of feature points on the map and the position of the robot itself. In order to solve this problem, a new method based on the probability hypothesis density (PHD) SLAM is proposed in this paper, a PHD-SLAM Method for Mixed Birth Map Information Based on Amplitude Information (AI-MBMI-PHD-SLAM). Firstly, this paper proposes a PHD-SLAM method based on amplitude information (AI-PHD-SLAM). The method uses the amplitude information of map features to obtain more precise map features. Then, the clutter likelihood function is used to improve the estimation accuracy of the feature map in the SLAM process. Meanwhile, this paper studies the performance of the PHD-SLAM method with the amplitude information under the condition of the known signal-to-noise ratio or the unknown signal-to-noise ratio. Secondly, aiming at the problem that PHD-SLAM lacks a priori information in the prediction stage, an AI-PHD-SLAM-based mixed birth map information method is added. In this method, map information that has been detected before the previous moment is added to the observation information in the map prediction phase as a new map information set in the prediction phase. This can increase the prior information and improve the problem of insufficient prior information in the prediction stage. The results of the experiments show that the proposed method and the improved method outperform the RB-PHD-SLAM method in estimating the number and location accuracy of map features and have higher computational efficiency.
Highlights
With the development of integration technology, mobile robots have been widely used in various fields, such as indoor fire rescue and underwater exploration. erefore, navigation technology is important for mobile robots
The robot must use more auxiliary information to distinguish clutter and true features from observation information and obtain more accurate map features and clutter likelihood functions, so as to improve the accuracy of Simultaneous Localization and Mapping (SLAM) method. e amplitude information for filter was first proposed by Daniel Clark and others. ey provided the amplitude likelihood function under the known and unknown target signal-to-noise ratio and put forward an amplitude-aided probability hypothesis density (PHD) filtering method to improve the accuracy of multitarget state estimation [18,19,20,21,22]
Based on the above-mentioned AI-PHD method, this paper proposes an amplitude information-assisted PHD-SLAM method (AI-PHD-SLAM), which can improve the accuracy of PHD-SLAM estimation of map features and robot pose in the same clutter environment and alleviate the computational amount. en, in order to solve the problem of inaccurate map estimation caused by insufficient prior information, the method of mixing new map information is added on AI-PHD-SLAM method in this paper. is method takes the map features near the robot position in the detected map information into the last moment observation set as the new map feature set in the PHD-SLAM prediction process, which overcomes low accuracy of the map feature position and number estimation for lack of prior information
Summary
With the development of integration technology, mobile robots have been widely used in various fields, such as indoor fire rescue and underwater exploration. erefore, navigation technology is important for mobile robots. Ey provided the amplitude likelihood function under the known and unknown target signal-to-noise ratio and put forward an amplitude-aided PHD filtering (amplitude information PHD, AI-PHD) method to improve the accuracy of multitarget state estimation [18,19,20,21,22] This method has not been used for SLAM. Based on the above-mentioned AI-PHD method, this paper proposes an amplitude information-assisted PHD-SLAM method (AI-PHD-SLAM), which can improve the accuracy of PHD-SLAM estimation of map features and robot pose in the same clutter environment and alleviate the computational amount. Is method takes the map features near the robot position in the detected map information into the last moment observation set as the new map feature set in the PHD-SLAM prediction process, which overcomes low accuracy of the map feature position and number estimation for lack of prior information.
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