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Abstract
To solve the illumination sensitivity problems of mobile ground equipment, an enhanced visual SLAM algorithm based on the sparse direct method was proposed in this paper. Firstly, the vignette and response functions of the input sequences were optimized based on the photometric formation of the camera. Secondly, the Shi–Tomasi corners of the input sequence were tracked, and optimization equations were established using the pixel tracking of sparse direct visual odometry (VO). Thirdly, the Levenberg–Marquardt (L–M) method was applied to solve the joint optimization equation, and the photometric calibration parameters in the VO were updated to realize the real-time dynamic compensation of the exposure of the input sequences, which reduced the effects of the light variations on SLAM’s (simultaneous localization and mapping) accuracy and robustness. Finally, a Shi–Tomasi corner filtered strategy was designed to reduce the computational complexity of the proposed algorithm, and the loop closure detection was realized based on the oriented FAST and rotated BRIEF (ORB) features. The proposed algorithm was tested using TUM, KITTI, EuRoC, and an actual environment, and the experimental results show that the positioning and mapping performance of the proposed algorithm is promising.
Highlights
Many visual simultaneous localization and mapping (SLAM) systems have been proposed, since they are fundamental building blocks for many emerging technologies, such as autonomous cars, virtual reality, and augmented reality [1]
We demonstrated that the drift error of the proposed algorithm was significantly reduced with respect to LDSO and the performance on KITTI was similar to mono-ORBSLAM
The experimental results show that the real-time photometric calibration direct SLAM system can obtain a 19.7% higher accuracy performance and 4.7% bigger timing costs than LDSO
Summary
Many visual simultaneous localization and mapping (SLAM) systems have been proposed, since they are fundamental building blocks for many emerging technologies, such as autonomous cars, virtual reality, and augmented reality [1]. Bergmann et al [14] proposed an online photometric calibration, which dynamically estimates the photometric parameters by solving the least squares equation of the feature tracker and adjusts the exposure situation of the input sequence. It is a milestone in improving the positioning and mapping accuracy for direct formulation. Inspired by [14] and [17], in order to further improve the robustness of the direct formulation visual SLAM system in positioning and mapping, we reinforced the LDSO algorithm by introducing real-time photometric calibration to update the exposure condition of the input sequence.
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