Autonomous navigation of unmanned aerial vehicles (UAVs) in GPS‐denied environments is a challenging problem, especially for small‐scale UAVs characterized by a small payload and limited battery autonomy. A possible solution to the aforementioned problem is vision‐based simultaneous localization and mapping (SLAM), since cameras, due to their dimensions, low weight, availability, and large information bandwidth, circumvent all the constraints of UAVs. In this paper, we propose a stereo vision SLAM yielding very accurate localization and a dense map of the environment developed with the aim to compete in the European Robotics Challenges (EuRoC) targeting airborne inspection of industrial facilities with small‐scale UAVs. The proposed approach consists of a novel stereo odometry algorithm relying on feature tracking (SOFT), which currently ranks first among all stereo methods on the KITTI dataset. Relying on SOFT for pose estimation, we build a feature‐based pose graph SLAM solution, which we dub SOFT‐SLAM. SOFT‐SLAM has a completely separate odometry and mapping threads supporting large loop‐closing and global consistency. It also achieves a constant‐time execution rate of 20 Hz with deterministic results using only two threads of an onboard computer used in the challenge. The UAV running our SLAM algorithm obtained the highest localization score in the EuRoC Challenge 3, Stage IIa–Benchmarking, Task 2. Furthermore, we also present an exhaustive evaluation of SOFT‐SLAM on two popular public datasets, and we compare it to other state‐of‐the‐art approaches, namely ORB‐SLAM2 and LSD‐SLAM. The results show that SOFT‐SLAM obtains better localization accuracy on the majority of datasets sequences, while also having a lower runtime.

No abstract

In this paper we propose a new state estimation algorithm called the extended information filter on Lie groups. The proposed filter is inspired by the extended Kalman filter on Lie groups and exhibits the advantages of the information filter with regard to multisensor update and decentralization, while keeping the accuracy of stochastic inference on Lie groups. We present the theoretical development and demonstrate its performance on multisensor rigid body attitude tracking by forming the state space on the SO(3) × R 3 group, where the first and second component represent the orientation and angular rates, respectively. The performance of the filter is compared with respect to the accuracy of attitude tracking with parametrization based on Euler angles and with respect to execution time of the extended Kalman filter formulation on Lie groups. The results show that the filter achieves higher performance consistency and smaller error by tracking the state directly on the Lie group and that it keeps smaller computational complexity of the information form with respect to high number of measurements.

No abstract

No abstract