  Autonomous driving systems are set to become a reality in transport systems
and, so, maximum acceptance is being sought among users. Currently, the most
advanced architectures require driver intervention when functional system
failures or critical sensor operations take place, presenting problems related
to driver state, distractions, fatigue, and other factors that prevent safe
control. Therefore, this work presents a redundant, accurate, robust, and
scalable LiDAR odometry system with fail-aware system features that can allow
other systems to perform a safe stop manoeuvre without driver mediation. All
odometry systems have drift error, making it difficult to use them for
localisation tasks over extended periods. For this reason, the paper presents
an accurate LiDAR odometry system with a fail-aware indicator. This indicator
estimates a time window in which the system manages the localisation tasks
appropriately. The odometry error is minimised by applying a dynamic 6-DoF
model and fusing measures based on the Iterative Closest Points (ICP),
environment feature extraction, and Singular Value Decomposition (SVD) methods.
The obtained results are promising for two reasons: First, in the KITTI
odometry data set, the ranking achieved by the proposed method is twelfth,
considering only LiDAR-based methods, where its translation and rotation errors
are 1.00% and 0.0041 deg/m, respectively. Second, the encouraging results of
the fail-aware indicator demonstrate the safety of the proposed LiDAR odometry
system. The results depict that, in order to achieve an accurate odometry
system, complex models and measurement fusion techniques must be used to
improve its behaviour. Furthermore, if an odometry system is to be used for
redundant localisation features, it must integrate a fail-aware indicator for
use in a safe manner.
