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The emergence of bio-inspired event cameras has opened up new exciting possibilities in high-frequency tracking, overcoming some of the limitations of traditional framebased vision (e.g. motion blur during high-speed motions or saturation in scenes with high dynamic range). As a result, research has been focusing on the processing of their unusual output: an asynchronous stream of events. With the majority of existing techniques discretizing the event-stream into frame-like representations, we are yet to harness the true power of these cameras.In this paper, we propose the ACE tracker: a purely asynchronous framework to track corner-event features. Evaluation on benchmarking datasets reveals significant improvements in accuracy and computational efficiency in comparison to state-of-the-art event-based trackers. ACE achieves robust performance even in challenging scenarios, where traditional frame-based vision algorithms fail.Videohttps://youtu.be/I31yQqmCsfs

Small Unmanned Aerial Vehicles (UAVs) are some of the most promising robotic platforms in a variety of applications due to their high mobility. Their restricted computational and payload capabilities, however, translate into significant challenges in automating their navigation. With Simultaneous Localization And Mapping (SLAM) systems recently demonstrated to be employable onboard UAVs, the focus fall on path-planning on the quest of achieving autonomous navigation. With the vast body of path-planning literature often assuming perfect maps or maps known a priori, the biggest challenge lies in dealing with the robustness and accuracy limitations of onboard SLAM in real missions. In this spirit, this paper proposes a path-planning algorithm designed to work in the loop of the SLAM estimation of a monocular-inertial system. This point-to-point planner is demonstrated to navigate in an unknown environment using the incrementally generated SLAM map, while dictating the navigation strategy for preferable acquisition of sensor data for better estimations within SLAM. A thorough evaluation testbed of both simulated and real data is presented, demonstrating the robustness of the proposed pipeline against the state-of-the-art and its dramatically lower computational complexity, revealing its suitability to UAV navigation.

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In this paper, we introduce IDOL, an optimization-based framework for IMU-DVS Odometry using Lines. Event cameras, also called Dynamic Vision Sensors (DVSs), generate highly asynchronous streams of events triggered upon illumination changes for each individual pixel. This novel paradigm presents advantages in low illumination conditions and high-speed motions. Nonetheless, this unconventional sensing modality brings new challenges to perform scene reconstruction or motion estimation. The proposed method offers to leverage a continuous-time representation of the inertial readings to associate each event with timely accurate inertial data. The method's front-end extracts event clusters that belong to line segments in the environment whereas the back-end estimates the system's trajectory alongside the lines' 3D position by minimizing point-to-line distances between individual events and the lines' projection in the image space. A novel attraction/repulsion mechanism is presented to accurately estimate the lines' extremities, avoiding their explicit detection in the event data. The proposed method is benchmarked against a state-of-the-art frame-based visual-inertial odometry framework using public datasets. The results show that IDOL performs at the same order of magnitude on most datasets and even shows better orientation estimates. These findings can have a great impact on new algorithms for DVS.