Mobile Robot Localization: a Modular, Odometry-Improving Approach

Despite the number of works published in recent years, vehicle localization remains an open, challenging problem. While map-based localization and SLAM algorithms are getting better and better, they remain a single point of failure in typical localization pipelines. This paper proposes a modular localization architecture that fuses sensor measurements with the outputs of off-the-shelf localization algorithms. The fusion filter estimates model uncertainties to improve odometry in case absolute pose measurements are lost entirely. The architecture is validated experimentally on a real robot navigating autonomously proving a reduction of the position error of more than 90% with respect to the odometrical estimate without uncertainty estimation in a two-minute navigation period without position measurements.

Automatic Navigation Map Generation for Mobile Robots in Urban Environments

A fundamental prerequisite for safe and efficient navigation of mobile robots is the availability of reliable navigation maps upon which trajectories can be planned. With the increasing industrial interest in mobile robotics, especially in urban environments, the process of generating navigation maps has become of particular interest, being a labor intensive step of the deployment process. Automating this step is challenging and becomes even more arduous when the perception capabilities are limited by cost considerations. This paper proposes an algorithm to automatically generate navigation maps using a typical navigation-oriented sensor setup: a single top-mounted 3D LiDAR sensor. The proposed method is designed and validated with the urban environment as the main use case: it is shown to be able to produce accurate maps featuring different terrain types, positive obstacles of different heights as well as negative obstacles. The algorithm is applied to data collected in a typical urban environment with a wheeled inverted pendulum robot, showing its robustness against localization, perception and dynamic uncertainties. The generated map is validated against a human-made map.