# Towards Improved Inertial Navigation by Reducing Errors Using Deep Learning Methodology
###### Abstract
As a result, more complicated data-processing applications can be mapped into deep learning networks. Many research groups have applied different kinds of deep learning networks to the field of autonomous driving including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Deep Reinforcement Learning (DRL) [45]. [PERSON] et al. [46] used CNN to detect road anomalies by using accelerometers from IMU. The group tested with two window sizes for CNN data sampling: an 85 window size (1.9 s) for automatic pothole detection and a 100 window size (2 s) for road roughness classification. Following the work by [PERSON] et al. [46], [PERSON] et al. [47] applied CNN on the raw data collected directly from the accelerometers. The team proposed time-frequency representation-based inputs for their CNN approach and transformed the accelerometer data into a spectrogram, and then fed the spectral representation to a CNN. By using this approach, the research group achieved the accuracy of up to 97.2% to detect road anomalies. Other research groups have proposed the use of RNN which is based on feedforward construction that makes use of sequential information from previous moments [48; 49]. It is generally believed that RNN adds memory into the system solution and is useful in recognition of patterns in sequences of data such as text, genomes, the spoken word, and numerical-time-series data. [PERSON] et al. [50] proposed a new CNN- and RNN-based Visual Odometry (VO) application to generate tracking trajectories for uncertain situations in a smart driving car. Several research groups have also proposed LSTM algorithms for sensor error compensation and sensor fusion [51]. For example, [PERSON] et al. [52] developed a new deep neural network based on VO by using low-cost IMUs. The team replaced the RNN with the LSTM and trained the combined networks with the raw measurements collected from IMUs. By applying LSTMs, the research group demonstrated that the new network achieved better estimated trajectories for nonperiodic motion in highly dynamic condition. [PERSON] et al. [53] proposed a sensor fusion architecture with IMU and 2D laser scanner by applying both CNNs and LSTMs. This system consisted of three networks: a CNN-based point cloud feature extraction from two laser scans, an LSTM-based IMU sequence registration, and an LSTM-based data fusion. Besides sensor fusion application, several research groups have explored methods of IMU denoising using LSTM. [PERSON] et al. [54] proposed an LSTM-based error-modelling methodology to identify the random errors in a gyroscope. The research team configured the LSTM network with five different lengths for input vectors. From the experimental results, the team demonstrated that a single-layer LSTM network can reduce the standard deviation by up to 42.4%, and the attitude errors by up to 52.0%. [PERSON] et al. [55] first introduced Gated Recurrent Unit (GRU), which is another variant of RNN, for gyroscope noise suppression. GRUs are similar to LSTM, with fewer components than LSTM. With the benefit of fewer parameters, GRUs have fast training speed and relatively better performance for certain tasks. [PERSON] et al. [56] proposed several LSTM and GRU hybrid network architectures and compared denoising performances along with LSTM-only and GRU-only networks. From the demonstrated results, the team achieved up to 72% decrease in attitude errors by using LSTM-GRU hybrid network. The above-described prior works are based on supervised learning, by which the networks are typically trained by labelled datasets to perform classification and regression tasks. By contrast, with the environment of nonprovided labelled data, an unsupervised learning algorithm is needed. Reinforcement learning (RL) is applied to learn the features from collected data through estimations of trial and error [57; 58; 59]. One recent work has focused on applying deep RL to the field of inertial sensors and autonomous driving.