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This paper presents evaluation of four different state estimation architectures exploiting the extended Kalman filter (EKF) for 6-DOF dead reckoning of a mobile robot. The EKF is a well proven and commonly used technique for fusion of inertial data and robot's odometry. However, different approaches to designing the architecture of the state estimator lead to different performance and computational demands. While seeking the best possible solution for the mobile robot, the nonlinear model and the error model are addressed, both with and without a complementary filter for attitude estimation. The performance is determined experimentally by means of precision of both indoor and outdoor navigation, including complex-structured environment such as stairs and rough terrain. According to the evaluation, the nonlinear model combined with the complementary filter is selected as a best candidate (reaching 0.8 m RMSE and average of 4% return position error (RPE) of distance driven) and implemented for real-time onboard processing during a rescue mission deployment. Index Terms-Complementary filter (CF), extended Kalman filter (EKF), urban search and rescue (USAR).

Urban search and rescue (USAR) missions for mobile robots require reliable state estimation systems resilient to conditions given by the dynamically changing environment. We design and evaluate a data fusion system for localization of a mobile skid‐steer robot intended for USAR missions. We exploit a rich sensor suite including both proprioceptive (inertial measurement unit and tracks odometry) and exteroceptive sensors (omnidirectional camera and rotating laser rangefinder). To cope with the specificities of each sensing modality (such as significantly differing sampling frequencies), we introduce a novel fusion scheme based on an extended Kalman filter for six degree of freedom orientation and position estimation. We demonstrate the performance on field tests of more than 4.4  km driven under standard USAR conditions. Part of our datasets include ground truth positioning, indoor with a Vicon motion capture system and outdoor with a Leica theodolite tracker. The overall median accuracy of localization—achieved by combining all four modalities—was 1.2% and 1.4% of the total distance traveled for indoor and outdoor environments, respectively. To identify the true limits of the proposed data fusion, we propose and employ a novel experimental evaluation procedure based on failure case scenarios. In this way, we address the common issues such as slippage, reduced camera field of view, and limited laser rangefinder range, together with moving obstacles spoiling the metric map. We believe such a characterization of the failure cases is a first step toward identifying the behavior of state estimation under such conditions. We release all our datasets to the robotics community for possible benchmarking.

We provide key facts about the TRADR project deployment of ground and aerial robots in Amatrice, Italy, after the major earthquake in August 2016. The robots were used to collect data for 3D textured models of the interior and exterior of two badly damaged churches of high national heritage valu

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