Title: Redundant Perception and State Estimation for Reliable Autonomous Racing,
Abstract: In autonomous racing, vehicles operate close to the limits of handling and a
sensor failure can have critical consequences. To limit the impact of such
failures, this paper presents the redundant perception and state estimation
approaches developed for an autonomous race car. Redundancy in perception is
achieved by estimating the color and position of the track delimiting objects
using two sensor modalities independently. Specifically, learning-based
approaches are used to generate color and pose estimates, from LiDAR and camera
data respectively. The redundant perception inputs are fused by a particle
filter based SLAM algorithm that operates in real-time. Velocity is estimated
using slip dynamics, with reliability being ensured through a probabilistic
failure detection algorithm. The sub-modules are extensively evaluated in
real-world racing conditions using the autonomous race car "gotthard
driverless", achieving lateral accelerations up to 1.7G and a top speed of
90km/h.