Abstract
Self-driving cars have experienced rapid development in the past few years, and Simultaneous Localization and Mapping (SLAM) is considered to be their basic capabilities. In this article, we propose a direct vision LiDAR fusion SLAM framework that consists of three modules. Firstly, a two-staged direct visual odometry module, which consists of a frame-to-frame tracking step, and an improved sliding window based thinning step, is proposed to estimate the accurate pose of the camera while maintaining efficiency. Secondly, every time a keyframe is generated, a dynamic objects considered LiDAR mapping module is utilized to refine the pose of the keyframe to obtain higher positioning accuracy and better robustness. Finally, a Parallel Global and Local Search Loop Closure Detection (PGLS-LCD) module that combines visual Bag of Words (BoW) and LiDAR-Iris feature is applied for place recognition to correct the accumulated drift and maintain a globally consistent map. We conducted a large number of experiments on the public dataset and our mobile robot dataset to verify the effectiveness of each module in our framework. Experimental results show that the proposed algorithm achieves more accurate pose estimation than the state-of-the-art methods.
Highlights
The latest activity in the field of self-driving car navigation is considered to be the revolutionary technology that will change people’s lives in many ways [1]
To further demonstrated the performance of the proposed Parallel Global and Local Search (PGLS)-loop closure detection (LCD) method, we evaluated the algorithm on the KITTI dataset which is commonly used for place recognition
We propose a novel direct visual LiDAR odometry and mapping framework that combines a monocular camera with sparse precise range measurements of
Summary
The latest activity in the field of self-driving car navigation is considered to be the revolutionary technology that will change people’s lives in many ways [1]. It triggered a series of reactions that aroused the automotive industry, and SLAM plays a key role in autonomous vehicles, especially in solving positioning problems in unfamiliar environments.Traditionally, the localization of autonomous vehicles rely on Global Navigation. Studying the use of SLAM technology to solve the positioning problem in the GNSS-denied environment is of great significance to the realization of autonomous driving. Great progress has been made in the field of visual SLAM using monocular cameras, including direct methods and feature-based indirect methods. The classical visual feature-based methods have matured, resulting in a stable visual SLAM method
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