Obstacle Detection and Avoidance System for Small UAVs using a LiDAR

Moffatt, Andrew; Platt, Eric; Mondragon, Brandon; Kwok, Aaron; Uryeu, Dennis; Bhandari, Subodh

doi:10.1109/icuas48674.2020.9213897

Cited by 21 publications

(9 citation statements)

References 10 publications

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“…Vision-based methods include obstacle avoidance methods based on LiDAR images, Time of Flight (ToF) images, binocular images, or monocular images. In [ 1 , 2 , 17 ], the authors used LiDAR for collision avoidance of a drone. However, the installation of many powerful sensors or high performance sensors results in an increase in the weight of a drone, which leads to an increase in energy consumption.…”

Section: Related Workmentioning

confidence: 99%

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Pix2Pix-Based Monocular Depth Estimation for Drones with Optical Flow on AirSim

Shimada

Nishikawa

Kong

et al. 2022

Sensors

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In this work, we propose a method for estimating depth for an image of a monocular camera in order to avoid a collision for the autonomous flight of a drone. The highest flight speed of a drone is generally approximate 22.2 m/s, and long-distant depth information is crucial for autonomous flights since if the long-distance information is not available, the drone flying at high speeds is prone to collisions. However, long-range, measurable depth cameras are too heavy to be equipped on a drone. This work applies Pix2Pix, which is a kind of Conditional Generative Adversarial Nets (CGAN). Pix2Pix generates depth images from a monocular camera. Additionally, this work applies optical flow to enhance the accuracy of depth estimation. In this work, we propose a highly accurate depth estimation method that effectively embeds an optical flow map into a monocular image. The models are trained with taking advantage of AirSim, which is one of the flight simulators. AirSim can take both monocular and depth images over a hundred meter in the virtual environment, and our model generates a depth image that provides the long-distance information than images captured by a common depth camera. We evaluate accuracy and error of our proposed method using test images in AirSim. In addition, the proposed method is utilized for flight simulation to evaluate the effectiveness to collision avoidance. As a result, our proposed method is higher accuracy and lower error than a state of work. Moreover, our proposed method is lower collision than a state of work.

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Section: Related Workmentioning

confidence: 99%

“…Typically, conventional solutions have employed distance sensors. For instance, Light Detection and Ranging (LiDAR) which can detect long distances are employed [ 1 , 2 ]. Depth cameras or stereo cameras are also employed to perceive distance [ 3 , 4 , 5 , 6 ].…”

Section: Introductionmentioning

confidence: 99%

Pix2Pix-Based Monocular Depth Estimation for Drones with Optical Flow on AirSim

Shimada

Nishikawa

Kong

et al. 2022

Sensors

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“…Practical Development  'Figure 5' shows the rudimentary data flow diagram of Protall which is subsequently followed by Droanll and Roadall sub-systems with some modifications and advancements as discussed in the above sections.  The data flow defines the set of instructions which would be followed by UAVs and automobiles as to detect objects and take appropriate decisions to avoid obstacles while moving [13].  Continuous proximity sensing is achieved with the smart usage of intelligent sensors strategically placed at optimal positions of the system.  If any obstacle is detected within its vicinity, the system decelerates the vehicle and alerts the user with the help of an alarm. The vehicle will correspondingly move at slow rate until the obstacle is within the field of view.…”

Section: 3mentioning

confidence: 99%

Protall (An Intelligent, multi-sensor, comprehensive obstacle avoidance system for automobiles and UAVs)

Dhelia¹,

Chaughule²,

Choraria³

et al. 2022

J. Phys.: Conf. Ser.

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In this era of Artificial Intelligence and Automation, manufacturing and testing of self-driving cars and autonomous delivery of parcels to the desired location with the help of UAVs have a considerable amount of growth in the industry. This advancement in technology also raises safety issues due to the failure of sensors to detect the object or sometimes because of the dynamic environment. Protall (Protect-all) is an integrated solution for UAV and automobile vehicles to provide ultimate safety to both itself and its surroundings. With the strategic sensor integration and its intelligent processing, it aims to produce controlled output and hence, ensures to prevent any possible failures from occurring. The system constantly reacts to the environment and maintains comprehensive interaction with the user thus, enabling it to handle any dynamic situation and hence, it emerges as an optimal solution for Vehicles and UAVs.

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“…Recent years have seen plenty of obstacle detection and tracking algorithms based on different sensors, such as LiDAR [10] [11], monocular camera [12][13], event camera [14][15], and depth camera [6][7]. The depth camera, one of the most popular sensors for small-size UAV navigation, can provide the robot with images and pointcloud data.…”

Section: Related Workmentioning

confidence: 99%

A real-time dynamic obstacle tracking and mapping system for UAV navigation and collision avoidance with an RGB-D camera

Xu¹,

Zhan²,

Chen³

et al. 2022

Preprint

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The real-time dynamic environment perception has become vital for autonomous robots in crowded spaces. Although the popular voxel-based mapping methods can efficiently represent 3D obstacles with arbitrarily complex shapes, they can hardly distinguish between static and dynamic obstacles, leading to the limited performance of obstacle avoidance. While plenty of sophisticated learning-based dynamic obstacle detection algorithms exist in autonomous driving, the quadcopter's limited computation resources cannot achieve real-time performance using those approaches. To address these issues, we propose a real-time dynamic obstacle tracking and mapping system for quadcopter obstacle avoidance using an RGB-D camera. The proposed system first utilizes a depth image with an occupancy voxel map to generate potential dynamic obstacle regions as proposals. With the obstacle region proposals, the Kalman filter and our continuity filter are applied to track each dynamic obstacle. Finally, the environment-aware trajectory prediction method is proposed based on the Markov chain using the states of tracked dynamic obstacles. We implemented the proposed system with our custom quadcopter and navigation planner. The simulation and physical experiments show that our methods can successfully track and represent obstacles in dynamic environments in real-time and safely avoid obstacles.

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Obstacle Detection and Avoidance System for Small UAVs using a LiDAR

Cited by 21 publications

References 10 publications

Pix2Pix-Based Monocular Depth Estimation for Drones with Optical Flow on AirSim

Pix2Pix-Based Monocular Depth Estimation for Drones with Optical Flow on AirSim

Protall (An Intelligent, multi-sensor, comprehensive obstacle avoidance system for automobiles and UAVs)

A real-time dynamic obstacle tracking and mapping system for UAV navigation and collision avoidance with an RGB-D camera

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