Wheel odometry-based car localization and tracking on vectorial map

In this paper, we present a car self-localization approach based on free inputs. We propose to use wheel speeds, which is available on most car through the CAN bus, and community developed road maps. A particle filter framework is used to achieve self-localization on a graph-based representation of a road map. Our results suggests that self-localization and tracking are feasible with these two inputs at a really low computational cost. Car self-localization is achieved with an averaged 5 m accuracy within a 100 km drivable road map on a 12 km sequence.