AUV localisation: a review of passive and active techniques

Maurelli, Francesco; Krupínski, Szymon; Xia, Xiang; Pétillot, Yvan

doi:10.1007/s41315-021-00215-x

Cited by 43 publications

(25 citation statements)

References 142 publications

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“…Paull et al (2013) presented a review of the commonly used sensors and general methods for AUV navigation and localization. More recently, Maurelli et al (2021) discussed active and passive localization techniques for AUVs. Sonar (e.g., imaging sonar, scanning profiling sonar, and multibeam sonar) and/or camera are used to bound the odometry drift from dead-reckoning system, that is, IMU or Doppler Velocity Log (DVL).…”

Section: Related Workmentioning

confidence: 99%

SVIn2: A multi-sensor fusion-based underwater SLAM system

Rahman

Rekleitis

2022

The International Journal of Robotics Research

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This paper presents SVIn2, a novel tightly-coupled keyframe-based Simultaneous Localization and Mapping (SLAM) system, which fuses Scanning Profiling Sonar, Visual, Inertial, and water-pressure information in a non-linear optimization framework for small and large scale challenging underwater environments. The developed real-time system features robust initialization, loop-closing, and relocalization capabilities, which make the system reliable in the presence of haze, blurriness, low light, and lighting variations, typically observed in underwater scenarios. Over the last decade, Visual-Inertial Odometry and SLAM systems have shown excellent performance for mobile robots in indoor and outdoor environments, but often fail underwater due to the inherent difficulties in such environments. Our approach combats the weaknesses of previous approaches by utilizing additional sensors and exploiting their complementary characteristics. In particular, we use (1) acoustic range information for improved reconstruction and localization, thanks to the reliable distance measurement; (2) depth information from water-pressure sensor for robust initialization, refining the scale, and assisting to limit the drift in the tightly-coupled integration. The developed software—made open source—has been successfully used to test and validate the proposed system in both benchmark datasets and numerous real world underwater scenarios, including datasets collected with a custom-made underwater sensor suite and an autonomous underwater vehicle Aqua2. SVIn2 demonstrated outstanding performance in terms of accuracy and robustness on those datasets and enabled other robotic tasks, for example, planning for underwater robots in presence of obstacles.

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Section: Related Workmentioning

confidence: 99%

SVIn2: A multi-sensor fusion-based underwater SLAM system

Rahman

Rekleitis

2022

The International Journal of Robotics Research

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“…The robot must then fuse these observations with a map of the environment and estimate its pose using techniques such as Kalman filters, particle filters, or SLAM (simultaneous localization and mapping). However, these techniques can suffer from drift, ambiguity, and inconsistency, especially in dynamic or cluttered environments, leading to erroneous or uncertain pose estimates [10].…”

Section: Introductionmentioning

confidence: 99%

Navigation of Autonomous Vehicles using Reinforcement Learning with Generalized Advantage Estimation

Jacinto¹,

Martínez²,

Sarmiento³

2023

IJACSA

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This study proposes a reinforcement learning approach using Generalized Advantage Estimation (GAE) for autonomous vehicle navigation in complex environments. The method is based on the actor-critic framework, where the actor network predicts actions and the critic network estimates state values. GAE is used to compute the advantage of each action, which is then used to update the actor and critic networks. The approach was evaluated in a simulation of an autonomous vehicle navigating through challenging environments and it was found to effectively learn and improve navigation performance over time. The results suggest GAE as a promising direction for further research in autonomous vehicle navigation in complex environments.

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“…For instance, in search and rescue operations the possibility of re-establishing the connectivity and localizing the ground terminals can save lives by accelerating the search activities and saving the workforce for the rescue activities. In general, the localization of targets can be classified as passive or active: in passive localization, targets do not emit signals useful for their position estimates and they are typically localized by employing radar, lidar, or other sensing technologies while, in active localization, targets emit Radio Frequency (RF) signals [3].…”

Section: Introductionmentioning

confidence: 99%

Experimental UAV-Aided RSSI Localization of a Ground RF Emitter in 865 MHz and 2.4 GHz Bands

Moro

Teeda

Scazzoli

et al. 2022

2022 IEEE 95th Vehicular Technology Conference: (VTC2022-Spring)

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Unmanned Aerial Vehicles (UAVs) can be used as low altitude platforms in several applications. In this paper, we propose their use to localize a ground Radio Frequency (RF) emitter by collecting measures of the Received Signal Strength Indicator (RSSI) at different positions. The main contribution of the work consists in the definition of an experimental setup for the simultaneous measures of RSSI and receiver position. The RSSI is measured by an actual transceiver, the Adalm Pluto Software Defined Radio (SDR) development board, programmed with the open-source software GNU Radio. The position is provided by GPS and Inertial Measuring Unit (IMU) sensors on the drone. The measures are acquired in the 865 MHz Short Range Device (SRD) and 2.4 GHz Industrial Scientific Medical (ISM) unlicensed frequency bands. Since the ISM measures can be affected by interference generated by different sources (e.g. Wi-Fi access points and UAV controller), the SRD band is exploited for collecting the RSSI measures with less interference. A maximum likelihood (ML) algorithm is applied to the collected data for estimating the transmitter location. For the considered setup we show that the mean absolute localization error is around 4 m without interference and 5 m with interference. A thresholdbased technique is proposed to improve the accuracy in presence of interference.

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AUV localisation: a review of passive and active techniques

Cited by 43 publications

References 142 publications

SVIn2: A multi-sensor fusion-based underwater SLAM system

SVIn2: A multi-sensor fusion-based underwater SLAM system

Navigation of Autonomous Vehicles using Reinforcement Learning with Generalized Advantage Estimation

Experimental UAV-Aided RSSI Localization of a Ground RF Emitter in 865 MHz and 2.4 GHz Bands

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