DeepVO Towards end to end visual odometry with deep RNN

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Visual Odometry (VO) is a computer vision technique for estimating an object’s position and orientation from camera images. It is an important technique commonly used for “pose estimation and robot localization”, with notable applications on the Mars Exploration Rovers and Autonomous Vehicles [x1] [x2]. While the research field of VO is broad, this paper focuses on the topic of monocular visual odometry. Particularly, the authors examine prominent VO methods and argue mainstream geometry based monocular VO methods should be amended with deep learning approaches. Subsequently, the paper proposes a novel deep-learning based end-to-end VO algorithm, and then empirically demonstrates its viability.

Related Work

Visual odometry algorithms can be grouped into two main categories. The first is known as the conventional methods, and they are based on established principles of geometry. Specifically, an object’s position and orientation are obtained by identifying reference points and calculating how those points change over the image sequence. Algorithms in this category can be further divided into two sparse feature based methods and direct methods, which differ by how they select reference points. On the one hand, sparse feature based methods establish reference points using image salient features, such as corners and edges [8]. Direct methods, on the other hand, make use of the whole image and consider every pixel as a reference point [11]. Recenly, semi-direct methods that combine the benefits of both approaches are gaining popularity [16].

Today, most of state-of-the-art VO algorithms belong to the geometry family. However, they suffer significant limitations. For example, direct methods assume “photometric consistency” [11]. Sparse feature methods are also prone to “drifting” because of outliers and noises. As a result, the paper argues that geometry-based methods are difficult to engineer and calibrate, limiting its practicality. Figure 1 illustrates the general architecture of geometry-based algorithms, and it outlines necessary drift correction techniques such as Camera Calibration, Feature Detection, Feature Matching (tracking), Outlier Rejection, Motion Estimation, Scale Estimation, and Local optimization (bundle adjustment).

DeepVO Figure 1.png

Figure 1. Architectures of the conventional geometry-based monocular VO method.

The second category of VO algorithms is based on learning. Namely, they try to learn an object’s motion model from labeled optical flows. Initially, these models are trained using classic Machine Learning techniques such as KNN [15], Gaussian Process [16], and Support Vector Machines[17]. However, these models were inefficient to handle highly non-linear and high-dimensional inputs, leading to poor performance in comparison with geometry-based methods. For this reason, Deep Learning-based approaches are dominating research in this field and producing many promising results. For example, CNN based models can now recognize places based on appearance [18] and detect direction and velocity from stereo inputs [20]. Moreover, a deep learning model even achieved robust VO with blurred and under-exposed images [21]. While these successes are encouraging, the authors observe that a CNN based architecture is “incapable of modeling sequential information”. Instead, they proposed to use RNN to tackle this problem.

End-to-End Visual odometry through RCNN

Architecture Overview

An end-to-end monocular VO model is proposed by utilizing deep Recurrence Convolutional Neural Network (RCNN). Figure 2 depicts the end-to-end model, which is comprised of three main stages. First, the model takes a monocular video as input and pre-processes the image sequences by “subtracting the mean RGB values of all frames” from each frame. Then, consecutive image sequences are stacked to form tensors, which become the inputs for the CNN stage. The purpose of the CNN stages is to extract salient features from the image tensors. The structure of the CNN is inspired by FlowNet [24], which is a model design to extract optical flows. Details of the CNN structure is shown in Table 1. Using CNN optical flow features as input, the RNN stage tries to estimate the temporal and sequential relations among the features. The RNN stage does this by utilizing two Long Short-Term Memory networks (LSTM), which estimate object poses for each time step using both long-term and short-term dependencies. Figure 3 illustrated the RNN architecture.

DeepVO Figure 2.png

Figure 2. Architectures of the proposed RCNN based monocular VO system.

DeepVO Table 1.png

Table 1. CNN structure

DeepVO Figure 3.png

Figure 3. Folded and unfolded LSTMs and its internal structure.

Training and Optimisation

The proposed RCNN model can be represented as a conditional probability of poses given an image sequence: p(Yt|Xt) = p(y1,...,yt|x1,...,xt). Given this probability function is expressed by a deep RCNN, the problem can be interpreted as finding the hyperparameters or network weights that minimize the loss function between actual and predicted poses. Such that, “the loss function is composed of Mean Square Error (MSE) of all positions and orientations”.

Experiments and Results

The paper evaluated the proposed RCNN VO model by comparing it empirically with the open-source VO library of LIBVISO2 [7], which is a well-known geometry based model. The comparison is carried out using the KITTI VO/SLAM benchmark [3]. In total, the KITTI VO/SLAM benchmark contains 22 image sequences, 11 of which are labeled with ground truths. Two separate experiments are performed.

1. Quantitatively Analysis is performed using only labeled image sequence. Namely, 4 of those images sequences were used for training and the others for testing. Table 2 and Figure 6 outlines the result, and they show that the proposed RCNN model performs consistently better than the monocular VISO2_M model. However, it performs worse than the stereo VISO2_S model.

DeepVO Table 2.png

DeepVO Figure 6.png

2. The generalizability of the proposed RCNN model in a new environment is evaluated using unlabeled image sequences. Figure 8 outlines the result, and it shows that the proposed model is able to generalize better than the monocular VISO2_M model and performs roughly the same as the stereo VISO2_S model.

DeepVO Figure 8.png


The paper presents a new RCNN VO model that combines the CNNs with the RNNs. Although it is considered a viable approach, it is not expected to be a replacement to the classic geometry-based approach. The main contribution of the paper is threefold: 1) The authors demonstrate that the monocular VO problem can be addressed in an end-to-end fashion based on DL, i.e., directly estimating poses from raw RGB images. Neither prior knowledge nor parameter is needed to recover the absolute scale. 2) The authors propose a RCNN architecture enabling the DL based VO algorithm to be generalised to totally new environments by using the geometric feature representation learnt by the CNN. 3) Sequential dependence and complex motion dynamics of an image sequence, which are of importance to the VO but cannot be explicitly or easily modelled by human, are implicitly encapsulated and automatically learnt by the RCNN.


This paper cannot be considered as a critical advance to the state of the art as the authors just suggest a method combining CNN and RNNs for the visual odometry problem. The authors also state that deep learning in terms of simple feed-forward Neural networks and CNNs has already been used in this problem. Only an RNN approach seems to have been not tried on this problem. The authors propose a combined RCNN and geometric-based approach towards the end of the paper. But, it is not intuitive how these two potentially very diverse methods could be combined. The authors also do not explain any proposed methods for the combination. The authors don't build a compelling case against the state of the art methods or convincingly prove the superiority of the RCNN or a combined method. For example, the RCNN and other state of the art geometry-based methods have a deficiency of getting lower accuracies when shown a large open area in the images as mentioned by the authors. The authors put forth some techniques to solve this problem for the geometry approaches but they state that they do not have a similar method for the deep learning based approaches. Thus, in such scenarios, the methods proposed by the authors don't seem to work at all.


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