ATFVO: An Attentive Tensor-compressed LSTM Model with Optical Flow Features for Monocular Visual Odometry

Abstract

This paper proposes a new framework called ATFVO which can be deployed on the edge device to resolve monocular visual odometry problem. The vast majority of visual odometry algorithms using deep learning are equivalent to or beyond the traditional visual odometry algorithms in performance, however they do not consider the computing capability of edge equipment. In this paper, convolution neural network (CNN) and attentive tensor-compressed compression LSTM (A-T-LSTM) are used, with optical flow feature as input and a 6-DoF absolute-scale pose as output. The framework is fused with the spatio-temporal feature and deal with the overfitting problem of over-parameterized LSTM with high-dimensional inputs, and utilizes attention mechanism to get poses from the sequence output of T-LSTM. The poses are estimated from the original RGB images sequence without depending on any prior knowledge. The experimental outcomes at the KITTI dataset display that, in compared with the performance of the most advanced methods, the single T-LSTM model is 141× smaller than the original LSTM model, and the entire model is nearly one-seventh of DeepVO with a speed 23× faster than Flowdometry. The proposed VO is deployed to the robot based on raspberry pi, which can achieve real-time inference and navigate a cruise.