Robust Onboard Visual SLAM for Autonomous MAVs

Abstract. This paper presents a visual simultaneous localization and mapping (SLAM) system consisting of a robust visual odometry and an efficient back-end with loop closure detection and pose-graph optimization. Robustness of the visual odometry is achieved by utilizing dual cameras pointing different directions with no overlap in their respective fields of view mounted on an micro aerial vehicle (MAV). The theory behind this dual-camera visual odometry can be easily ex-tended to applications with multiple cameras. The back-end of the SLAM system maintains a keyframe-based global map, which is used for loop closure detec-tion. An adaptive-window pose-graph optimization method is proposed to refine keyframe poses of the global map and thus correct pose drift that is inherent in the visual odometry. The position of each map point is then refined implicitly due to its relative representation to its source keyframe. We demonstrate the efficiency of the proposed visual SLAM algorithm for applications onboard MAVs in ex-periments with both autonomous and manual flights. The pose tracking results are compared with the ground truth data provided by an external tracking system.

Aerial-Ground collaborative sensing: Third-Person view for teleoperation

Rapid deployment and operation are key requirements in time critical application, such as Search and Rescue (SaR). Efficiently teleoperated ground robots can support first-responders in such situations. However, first-person view teleoperation is sub-optimal in difficult terrains, while a third-person perspective can drastically increase teleoperation performance. Here, we propose a Micro Aerial Vehicle (MAV)-based system that can autonomously provide third-person perspective to ground robots. While our approach is based on local visual servoing, it further leverages the global localization of several ground robots to seamlessly transfer between these ground robots in GPS-denied environments. Therewith one MAV can support multiple ground robots on a demand basis. Furthermore, our system enables different visual detection regimes, and enhanced operability, and return-home functionality. We evaluate our system in real-world SaR scenarios.Comment: Accepted for publication in 2018 IEEE International Symposium on Safety, Security and Rescue Robotics (SSRR

Fast, Autonomous Flight in GPS-Denied and Cluttered Environments

One of the most challenging tasks for a flying robot is to autonomously navigate between target locations quickly and reliably while avoiding obstacles in its path, and with little to no a-priori knowledge of the operating environment. This challenge is addressed in the present paper. We describe the system design and software architecture of our proposed solution, and showcase how all the distinct components can be integrated to enable smooth robot operation. We provide critical insight on hardware and software component selection and development, and present results from extensive experimental testing in real-world warehouse environments. Experimental testing reveals that our proposed solution can deliver fast and robust aerial robot autonomous navigation in cluttered, GPS-denied environments.Comment: Pre-peer reviewed version of the article accepted in Journal of Field Robotic