Learning to Localise Automated Vehicles in Challenging Environments Using Inertial Navigation Systems (INS)

Abstract

An approach based on Artificial Neural Networks is proposed in this paper to improve the localisation accuracy of Inertial Navigation Systems (INS)/Global Navigation Satellite System (GNSS) based aided navigation during the absence of GNSS signals. The INS can be used to continuously position autonomous vehicles during GNSS signal losses around urban canyons, bridges, tunnels and trees, however, it suffers from unbounded exponential error drifts cascaded over time during the multiple integrations of the accelerometer and gyroscope measurements to position. More so, the error drift is characterised by a pattern dependent on time. This paper proposes several efficient neural network-based solutions to estimate the error drifts using Recurrent Neural Networks, such as the Input Delay Neural Network (IDNN), Long Short-Term Memory (LSTM), Vanilla Recurrent Neural Network (vRNN), and Gated Recurrent Unit (GRU). In contrast to previous papers published in literature, which focused on travel routes that do not take complex driving scenarios into consideration, this paper investigates the performance of the proposed methods on challenging scenarios, such as hard brake, roundabouts, sharp cornering, successive left and right turns and quick changes in vehicular acceleration across numerous test sequences. The results obtained show that the Neural Network-based approaches are able to provide up to 89.55% improvement on the INS displacement estimation and 93.35% on the INS orientation rate estimation.

Vehicular Visible Light Positioning Using Receiver Diversity with Machine Learning

“…Using the optimum vehicular VLP structure deduced in this work, we optimise different ML models to select the best fit for this application as seen in Table 3 [15,23]. The models considered are GRU, LSTM, sRNN and MLP.…”

“…In our previous work [14], we proposed the use of receiver diversity and supervised artificial neural network (ANN) to solve this aforementioned issue. We extended the work in [14] by using spatial and angular diversities with different machine learning (ML) approaches such as simple recurrent neural network (sRNN), gated recurrent unit (GRU) and long short term memory (LSTM) [15] to accurately estimate the position irrespective of the relative locations of the streetlights and further explore the effect of different weather conditions. To the best of the authors' knowledge, this paper is the first study of VLP with a linear array of streetlights using receiver diversity for autonomous vehicle and other outdoor applications.…”

A CNN-Based System for Mobile Robot Navigation in Indoor Environments via Visual Localization with a Small Dataset

“…One of the most fundamental technologies for automating these platforms is their navigation system [1]. Autonomous mobile robot navigation is an active area of research and has gained the great attention of numerous researchers in the recent past [2,3]. Autonomous Mobile Robots (AMR) are designed and programmed to perform multiple tasks in substitution for humans.…”

A Hybrid Data Fusion Approach to AI-Assisted Indirect Centralized Integrated SINS/GNSS Navigation System During GNSS Outage

“…In [20], a recurrent neural network (RNN) is utilized to predict SINS error during a 15-s GNSS blockage. Several neural network-based solutions such as the input delay neural network (IDNN), long shortterm memory (LSTM), vanilla recurrent neural network (VRNN), and gated recurrent unit (GRU) are proposed in [21] to predict the error drifts and SINS errors in challenging environments during a 10-s GNSS signal outage. Although mentioned methods have been extensively used during GNSS outages, their performance not only is dependent on proper selection of the artificial neural network (ANN)-model but also needs the accurate learning of the ANN network when GNSS exists.…”