  Low-cost inertial navigation sensors (INS) can be exploited for a reliable
tracking solution for autonomous vehicles. However, position errors grow
exponentially due to noises in the measurements. Several deep learning
techniques have been investigated to mitigate the errors for a better
navigation solution [1-10]. However, these studies have involved the use of
different datasets not made publicly available. The lack of a robust benchmark
dataset has thus hindered the advancement in the research, comparison and
adoption of deep learning techniques for vehicle positioning based on inertial
navigation. In order to facilitate the benchmarking, fast development and
evaluation of positioning algorithms, we therefore present the first of its
kind large-scale and information-rich inertial and odometry focused public
dataset called IO-VNBD (Inertial Odometry Vehicle Navigation Benchmark
Dataset).The vehicle tracking dataset was recorded using a research vehicle
equipped with ego-motion sensors on public roads in the United Kingdom,
Nigeria, and France. The sensors include a GPS receiver, inertial navigation
sensors, wheel-speed sensors amongst other sensors found on the car as well as
the inertial navigation sensors and GPS receiver in an android smart phone
sampling at 10HZ. A diverse number of scenarios and vehicle dynamics are
captured such as traffic, round-abouts, hard-braking etc. on different road
types (country roads, motorways etc.) with varying driving patterns. The
dataset consists of a total driving time of about 40 hours over 1,300km for the
vehicle extracted data and about 58 hours over 4,400 km for the smartphone
recorded data. We hope that this dataset will prove valuable in furthering
research on the correlation between vehicle dynamics and its displacement as
well as other related studies
