Research on Multi-Sensor Fusion SLAM Algorithm Based on Improved Gmapping

Abstract

Simultaneous Localization and Mapping (SLAM) is the core technology of the intelligent robot system, and it is also the basis for its autonomous movement. In recent years, it has been found that SLAM using a single sensor has certain limitations, such as Inertial Measurement Unit (IMU) noise and serious drift, and 2D radar can only detect environmental information on the same horizontal plane. In this regard, this paper constructs a multi-sensor back-end fusion SLAM algorithm that combines vision, laser, encoder and IMU information. Experiments have proved that compared with using a single sensor, the application of a multi-sensor fusion system makes the edges of the constructed map clearer and the noise reduced. Aiming at the problem of increased calculation caused by particle degradation and too many particles, this paper improves the Gmappping algorithm, and uses the combination of selective resampling and Kullback-Leibler Distance (KLD) sampling to complete resampling. It has been proved by experiments that compared with the original algorithm of Gmapping, the application of the improved algorithm increases the particle convergence speed by 39.85% in the process of indoor mapping. Aiming at the problems that the traditional loop detection algorithm is easily affected by environmental factors, resulting in low detection accuracy, and the loop detection algorithm based on deep convolutional neural network has a large amount of calculation and takes a long time to detect. The main research of this paper is to apply a deep learning-based loop detection algorithm on the multi-sensor fusion framework, and use the combination of high-dimensional and low-dimensional features of the image for loop detection. This paper uses different algorithms to conduct comparative experiments on the dataset CityCentre. The experimental results show that compared with the traditional algorithms Bag of Words (BoW), AlexNet algorithm, VGG19 algorithm, and ResNet32 algorithm, the accuracy of the algorithm proposed in this paper has increased by 31.26%, 14.21%, 3.05%, and 1.56%, respectively. In addition, the comparison experiment results of SLAM mapping with the original Real-Time Appearance-Based Mapping (RTAB-MAP) algorithm prove that the loop closure detection algorithm based on deep learning proposed in this paper can enable the system to better build a globally consistent map, including more environmental information.

Low-Cost Multisensory Robot for Optimized Path Planning in Diverse Environments

“…The replacement of LIDAR with SONAR, using an Nvidia Nano Jetson, is a cost-effective solution for designing a robot with low hardware requirements. For example, a robot designed in [35] includes a 2D LIDAR Sensor, RGB-D Camera, Nvidia Jetson Nano Board, WiFi Module, Encoder Gearmotor, and McNam Wheel, a similarpurpose robot to ours in this paper, i.e., one that can move on a surface on an optimized path. The comparison of costs is shown in Table 2.…”

A multifunctional ship sorting and clearance robot implementation model

“…The classic SLAM algorithms applicable to our robots include Gmapping, Cartographer, Hector, etc. Simultaneous Localization and Mapping (SLAM) is a fundamental technology in intelligent robot systems that enables the robot to autonomously navigate and map its environment [7]. SLAM allows for the positioning of robots in the map while creating the map [8],which is necessary For our autonomous robots.…”

Real-Time SLAM Based on Dynamic Feature Point Elimination in Dynamic Environment

“…Control strategy and environment modeling for robots is a topic known as Active Simultaneous Localization and Mapping (SLAM) [20]. According to the types of sensors employed, which are classified as LIDAR SLAM [21], Visual SLAM [22] and Multisensor Fusion SLAM [23], visual sensors are frequently used in the field of SALM due to its advantages of gathering a wealth of information and consuming little power.…”