A temporally consistent grid-based visual odometry framework for multi-core architectures

Most recent visual odometry algorithms based on sparse feature matching are computationally efficient methods that can be executed in real time on desktop computers. However, further efforts are required to reduce computational complexity in order to integrate these solutions in embedded platforms with low power consumption. This paper presents a spacetime framework that can be applied to most stereo visual odometry algorithms greatly reducing their computational complexity. Moreover, it enables exploiting multi-core architectures available in most modern computing platforms. According to the tests performed on publicly available datasets and an experimental driverless car, the proposed framework significantly reduces the computational complexity of a visual odometry algorithm while improving the accuracy of the results.