This paper presents a robust localization approach that fuses measurements from inertial measurement unit (IMU) and a rotating laser scanner. An Error State Kalman Filter (ESKF) is used for sensor fusion and is combined with a Gaussian Particle Filter (GPF) for measurements update. We experimentally demonstrated the robustness of this implementation in various challenging situations such as kidnapped robot situation, laser range reduction and various environment scales and characteristics. Additionally, we propose a new method to evaluate localizability of a given 3D map and show that the computed localizability can precisely predict localization errors, thus helps to find safe routes during flight.

As robots aspire for long-term autonomous operations in complex dynamic environments, the ability to reliably take mission-critical decisions in ambiguous situations becomes critical. This motivates the need to build systems that have situational awareness to assess how qualified they are at that moment to make a decision. We call this self-evaluating capability as introspection. In this paper, we take a small step in this direction and propose a generic framework for introspective behavior in perception systems. Our goal is to learn a model to reliably predict failures in a given system, with respect to a task, directly from input sensor data. We present this in the context of vision-based autonomous MAV flight in outdoor natural environments, and show that it effectively handles uncertain situations.