Visual SLAM Algorithm Based on Memory Parking Scene

doi:10.12141/j.issn.1000-565X.230262

Abstract

Abstract:

With the development of autonomous driving technology, visual simultaneous localization and mapping (SLAM) technology has attracted more and more attention. In the memory parking scene, it is necessary to establish a prior map of the parking lot scene. Thus, when the car enters the same parking lot again, visual SLAM can help to construct and locate the scene. In order to improve the robustness, accuracy and efficiency of the map built by SLAM, first, a lightweight deep learning algorithm is used to improve the poor robustness of the traditional feature extraction algorithms in different scenarios, and the deep separable convolution is adopted to replace the previous common convolution structure, which greatly improves the time efficiency of feature extraction. Next, the Patch-NetVLAD algorithm is improved based on ResNet network, and the improved residual network as well as the original VGG network is retrained on MSLS data set. Then, image retrieval is used for rough positioning, candidate image frames are selected, and camera pose is solved by fine positioning to complete global initialization relocation. On this basis, the improved bag of words algorithm is used to retrain the images in different parking lot scenes, and all the algorithms are transplanted into the OpenVSLAM architecture to complete the mapping and positioning of the actual scene. The experimental results show that the proposed visual SLAM system can complete the construction of many scenes such as aboveground, underground and semienclosed parking lots, with an average longitudinal positioning error of 8.42 cm and an average horizontal positioning error of 8.30 cm, both of which meet the engineering requirements.

Key words: simultaneous localization and mapping, memory parking, deep learning, feature extraction, image retrieval

CLC Number:

U471.1

HU Xizhi, CUI Bofei, WANG Qin, et al. Visual SLAM Algorithm Based on Memory Parking Scene[J]. Journal of South China University of Technology(Natural Science Edition), 2024, 52(6): 1-11.

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结构名称		执行操作	关键参数
结构名称		输入输出通道数	核大小	步长
Block1	Conv1_a	DPconv+BN+LReLU	1，64	3 × 3	1
	Conv1_b	DPconv+BN+LReLU	64，64	3 × 3	1
	Pooling1	Max pooling	64，64	2 × 2	2
Block2	Conv2_a	DPconv+BN+LReLU	64，64	3 × 3	1
	Conv2_b	DPconv+BN+LReLU	64，64	3 × 3	1
	Pooling2	Max pooling	64，64	2 × 2	2
Block3	Conv3_a	DPconv+BN+LReLU	64，128	3 × 3	1
	Conv3_b	DPconv+BN+LReLU	128，128	3 × 3	1
	Pooling3	Max pooling	128，128	2 × 2	2
Block4	Conv4_a	DPconv+BN+LReLU	128，128	3 × 3	1
Block4	Conv4_b	DPconv+BN+LReLU	128，128	3 × 3	1

算法名称	模型参数量	模型计算量
SuperPoint算法	1.52×10⁶	1.58×10¹⁰
文中特征提取算法	2.28×10⁵	2.12×10⁹

算法名称	可重复率/%	位置误差/m
ORB	41.81	1.28
SIFT	42.20	1.17
SuperPoint	59.25	1.08
文中算法	54.48	1.19

算法名称	每帧检测时间/s	图片尺寸/（像素×像素）	平均每帧图像的特征点数量
ORB	0.004	1 280✕960	949
SIFT	0.198	1 280✕960	837
SuperPoint	0.679	1 280✕960	1 073
文中算法	0.355	1 280✕960	1 002

系统名称	RMSE	最大误差	最小误差	标准差	平均误差
OpenVSLAM	1.52	6.79	0.20	0.89	1.26
SuperPoint+OpenVSLAM	1.29	3.98	0.19	0.66	1.10
文中算法+OpenVSLAM	1.34	4.78	0.15	0.70	1.14