In this paper, a novel parallel indirect visual odometry (VO) system is proposed based on a newly designed map management method, key-frame selection, and a camera pose correction model, where the speeded-up robust features (SURF) algorithm is used to extract features from an image, and a linear exhaustive search (LES) algorithm is introduced to match features. To minimize computation time, a key-frame selection mechanism is proposed to distinguish key frames among the input images. Moreover, map management is proposed to filter out unstable landmarks and add features for a reliable estimation of the relative camera pose. In addition, estimation accuracy is improved by the camera pose correction model. To enhance computational efficiency of the VO system, the proposed approach is implemented on a TX2 embedded system with multiple graphics processing units (GPUs), taking advantage of a heterogeneous parallel computing architecture. To validate the performances of the proposed system, several experiments are conducted using an ASUS Xtion 3D camera and a laptop. Average errors of pose estimations are compared with those via the conventional VO to show the effectiveness of the proposed VO system. Thanks to the proposed approach, a real-time and reliable VO system can be established with low cost, low power consumption, high processing efficiency, and portability. The experimental results show that based on heterogeneous computing, the required computation time of the overall system with GPUs is approximately 80 times faster than that with only a CPU, when dealing with 80 features in the environment. To the best of our knowledge, this is the first released paper that implements an indirect VO on a CPU/GPU heterogeneous computing platform.
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