Abstract
One of the critical tasks required for fully autonomous functionality is the ability to achieve an accurate navigation solution; that is, to determine the platform position, velocity, and orientation. Various sensors, depending on the vehicle environment (air, sea, or land), are employed to achieve this goal. In parallel to the development of novel navigation and sensor fusion algorithms, machine-learning based algorithms are penetrating into the navigation and sensor fusion fields. An excellent example for this trend is pedestrian dead reckoning, used for indoor navigation, where both classical and machine learning approaches are used to improve the navigation accuracy. To facilitate machine learning algorithms’ derivation and validation for autonomous platforms, a huge quantity of recorded sensor data is needed. Unfortunately, in many situations, such datasets are not easy to collect or are not publicly available. To advance the development of accurate autonomous navigation, this paper presents the autonomous platforms inertial dataset. It contains inertial sensor raw data and corresponding ground truth trajectories. The dataset was collected using a variety of platforms including a quadrotor, two autonomous underwater vehicles, a land vehicle, a remote controlled electric car, and a boat. A total of 805.5 minutes of recordings were made using different types of inertial sensors, global navigation satellite system receivers, and Doppler velocity logs. After describing the sensors that were employed for the recordings, a detailed description of the conducted experiments is provided. The autonomous platform inertial dataset is available at: <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/ansfl/Navigation-Data-Project/</uri> .
Highlights
A fully autonomous platform requires the ability to achieve accurate navigation; that is, to determine position, velocity and orientation
global navigation satellite systems (GNSS)/inertial navigation system (INS) fusion is used in unmanned ground vehicles (UGV) as shown in [9], [10]
Parts of the quadrotor dataset were used in two different studies: 1) Based on the periodic motion trajectories included in the dataset, we proposed a framework for quadrotor navigation based only on inertial sensors, called quadrotor dead reckoning (QDR) [2] as an alternative solution to classical INS
Summary
A fully autonomous platform requires the ability to achieve accurate navigation; that is, to determine position, velocity and orientation. To facilitate the derivation and validation of a machine learning algorithm for autonomous platforms, a huge quantity of real recorded data is needed. To cope with this increasing demand alongside the advances in pure inertial navigation, several datasets, including pedestrian odometry datasets, were recorded and made publicly available. An autonomous navigation dataset, A2D2 dataset from [29], provided IMU and GPS recordings from a land vehicle. To advance the development of accurate autonomous navigation, this paper shares the results of collecting and analyzing the raw data and corresponding ground truth trajectories from the inertial sensors. The rest of the paper is organized as follows: Section II describes the sensors that were employed for the recordings, Section III provides a detailed description of the experiments, and Section IV gives the conclusions
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