FAST-Dynamic-Vision: Detection and Tracking Dynamic Objects with Event and Depth Sensing

He, Botao; Li, Haojia; Wu, Siyuan; Wang, Dong; Zhang, Zhiwei; Dong, Qianli; Xu, Chao; Gao, Fei

doi:10.1109/iros51168.2021.9636448

Cited by 21 publications

(11 citation statements)

References 16 publications

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“…PULSAR achieved a good level of agility, as demonstrated in the experiments of trajectory tracking and step position response. This enabled PULSAR to perform challenging tasks like avoiding highspeed dynamic obstacles, which are possible only in quadrotor UAVs (46,47) to date. For existing self-rotation UAVs (20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30)(31)(32)(33) and other underactuated UAVs with fewer than four propellers (40,(48)(49)(50)(51)(52), the main focuses were on design concept, vehicle configuration, and flying feasibility, whereas agility and the ability to avoid dynamic obstacles were not considered or demonstrated.…”

Section: Agilitymentioning

confidence: 99%

A self-rotating, single-actuated UAV with extended sensor field of view for autonomous navigation

et al. 2023

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Uncrewed aerial vehicles (UAVs) rely heavily on visual sensors to perceive obstacles and explore environments. Current UAVs are limited in both perception capability and task efficiency because of a small sensor field of view (FoV). One solution could be to leverage self-rotation in UAVs to extend the sensor FoV without consuming extra power. This natural mechanism, induced by the counter-torque of the UAV motor, has rarely been exploited by existing autonomous UAVs because of the difficulties in design and control due to highly coupled and nonlinear dynamics and the challenges in navigation brought by the high-rate self-rotation. Here, we present powered-flying ultra-underactuated LiDAR (light detection and ranging) sensing aerial robot (PULSAR), an agile and self-rotating UAV whose three-dimensional position is fully controlled by actuating only one motor to obtain the required thrust and moment. The use of a single actuator effectively reduces the energy loss in powered flights. Consequently, PULSAR consumes 26.7% less power than the benchmarked quadrotor with the same total propeller disk area and avionic payloads while retaining a good level of agility. Augmented by an onboard LiDAR sensor, PULSAR can perform autonomous navigation in unknown environments and detect both static and dynamic obstacles in panoramic views without any external instruments. We report the experiments of PULSAR in environment exploration and multidirectional dynamic obstacle avoidance with the extended FoV via self-rotation, which could lead to increased perception capability, task efficiency, and flight safety.

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Section: Agilitymentioning

confidence: 99%

A self-rotating, single-actuated UAV with extended sensor field of view for autonomous navigation

et al. 2023

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“…Reprinted, with permission from Gageik et al (2015), and © [2021] IEEE. Reprinted, with permission from He et al (2021). MAV, micro aerial vehicle.…”

Section: Mavs Onboard Sensingmentioning

confidence: 99%

“…(b) is from Falanga et al (2020) Reprinted with permission from AAAS, (a) © [2021] IEEE. Reprinted, with permission from He et al (2021). [Color figure can be viewed at wileyonlinelibrary.com]…”

Section: Mavs Onboard Obstacle Descriptionmentioning

confidence: 99%

A comprehensive survey on non‐cooperative collision avoidance for micro aerial vehicles: Sensing and obstacle detection

Lü

Fasano

Carrio³

et al. 2023

Journal of Field Robotics

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In recent years, unmanned aerial vehicles (UAVs) have been confirmed as a powerful tool for countless applications in nearly every industry, in which collision avoidance plays a vital role for micro aerial vehicles (MAVs) performing autonomous missions.The complexity of collision avoidance systems is directly related to the complexity of the navigation environment, the avoidance of highly uncertain obstacles being a more thoroughly researched critical aspect. The non-cooperative collision avoidance strategy is necessary when communication between the agents is unavailable, under the case with many existing passive obstacles and non-cooperative flying objects. In this article, we review the topic of non-cooperative collision avoidance for MAVs in dynamic environments by covering two main issues: onboard sensing and obstacle description algorithms. A detailed discussion of these two topics is provided to face non-cooperative collision avoidance technology for MAVs. Finally, the paper summarizes the open challenges and foreseen future directions in this field.

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“…Finally, motion-segmentation algorithms have also been proposed to segment targets moving differently than the camera motion itself [18], [19], [20]. The algorithms perform best with constant camera motion, and with fast-moving, large targets.…”

Section: Introductionmentioning

confidence: 99%

PUCK: Parallel Surface and Convolution-kernel Tracking for Event-Based Cameras

Gava¹,

Monforte²,

Iacono³

et al. 2022

Preprint

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Low latency and accuracy are fundamental requirements when vision is integrated in robots for high-speed interaction with targets, since they affect system reliability and stability. In such a scenario, the choice of the sensor and algorithms is important for the entire control loop. The technology of event-cameras can guarantee fast visual sensing in dynamic environments, but requires a tracking algorithm that can keep up with the high data rate induced by the robot egomotion while maintaining accuracy and robustness to distractors. In this paper, we introduce a novel tracking method that leverages the Exponential Reduced Ordinal Surface (EROS) data representation to decouple event-by-event processing and tracking computation. The latter is performed using convolution kernels to detect and follow a circular target moving on a plane. To benchmark state-of-the-art event-based tracking, we propose the task of tracking the air hockey puck sliding on a surface, with the future aim of controlling the iCub robot to reach the target precisely and on time. Experimental results demonstrate that our algorithm achieves the best compromise between low latency and tracking accuracy both when the robot is still and when moving.

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FAST-Dynamic-Vision: Detection and Tracking Dynamic Objects with Event and Depth Sensing

Cited by 21 publications

References 16 publications

A self-rotating, single-actuated UAV with extended sensor field of view for autonomous navigation

A self-rotating, single-actuated UAV with extended sensor field of view for autonomous navigation

A comprehensive survey on non‐cooperative collision avoidance for micro aerial vehicles: Sensing and obstacle detection

PUCK: Parallel Surface and Convolution-kernel Tracking for Event-Based Cameras

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