Abstract
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 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.
Highlights
Global Positioning System (GPS) coverage problems derived from structural elements of the road, GPS multi-path in urban areas, or failure in its operation, mean that this technology does not meet the necessary localisation requirements in 100% of use-cases, which makes it essential to design redundant systems based on LiDAR odometry [1], Visual odometry [2], Inertial Navigation Systems (INS) [3], Wifi [4], or a combination of the above, including digital maps [5]
Another factor taken into account to enhance the odometry accuracy was to incorporate a 6-degrees of freedom (DoF) motion model based on vehicle dynamics and kinematics within the filter, where the variables of pitch and roll play a crucial impact on the precision
This paper presents a LiDAR odometry system with an integrated fail-aware feature, which notifies high-level systems with the actual performance of our proposal
Summary
The concept of autonomous driving is becoming more and more popular. new techniques are being developed and researched to help consolidate the reality of implementing the concept. Perception, localisation, or control systems are essential elements for their development They are susceptible to failures and it is necessary to have fail-x systems, which prevent undesired or fatal actions. Autonomous driving will be a closer reality when LiDAR or Visual odometry systems are integrated to cover fail-operational functions. GPS coverage problems derived from structural elements of the road (tunnels), GPS multi-path in urban areas, or failure in its operation, mean that this technology does not meet the necessary localisation requirements in 100% of use-cases, which makes it essential to design redundant systems based on LiDAR odometry [1], Visual odometry [2], Inertial Navigation Systems (INS) [3], Wifi [4], or a combination of the above, including digital maps [5]. It is necessary to introduce, for those systems that have a non-constant temporal drift, a fail-aware indicator to discern when these can be used
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