Towards Highly Reliable Autonomy for Urban Search and Rescue Robots

Kohlbrecher, Stefan; Kunz, Florian; Koert, Dorothea; Rose, Christian; Manns, Paul; Daun, Kevin; Schubert, Johannes; Stumpf, Alexander; Stryk, Oskar von

doi:10.1007/978-3-319-18615-3_10

Cited by 13 publications

(6 citation statements)

References 13 publications

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“…• Hector SLAM [Kohlbrecher et al, 2011] can create fast 2D occupancy grid maps from a 2D lidar with low computation resources. It has proven to generate very low-drift localization while map-ping in real-world autonomous navigation scenarios, like those in RoboCup Rescue Robot League competition [Kohlbrecher et al, 2016]. It can also use external sensors like an IMU to estimate the robot position in 3D.…”

Section: Popular Slam Approaches Available On Rosmentioning

confidence: 99%

RTAB‐Map as an open‐source lidar and visual simultaneous localization and mapping library for large‐scale and long‐term online operation

Labbé

Michaud

2018

Journal of Field Robotics

675

325

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Distributed as an open-source library since 2013, real-time appearance-based mapping (RTAB-Map) started as an appearance-based loop closure detection approach with memory management to deal with large-scale and long-term online operation. It then grew to implement simultaneous localization and mapping (SLAM) on various robots and mobile platforms. As each application brings its own set of constraints on sensors, processing capabilities, and locomotion, it raises the question of which SLAM approach is the most appropriate to use in terms of cost, accuracy, computation power, and ease of integration. Since most of SLAM approaches are either visual-or lidar-based, comparison is difficult. Therefore, we decided to extend RTAB-Map to support both visual and lidar SLAM, providing in one package a tool allowing users to implement and compare a variety of 3D and 2D solutions for a wide range of applications with different robots and sensors. This paper presents this extended version of RTAB-Map and its use in comparing, both quantitatively and qualitatively, a large selection of popular real-world datasets (e.g., KITTI, EuRoC, TUM RGB-D, MIT Stata Center on PR2 robot), outlining strengths, and limitations of visual and lidar SLAM configurations from a practical perspective for autonomous navigation applications. K E Y W O R D S perception, position estimation, SLAM

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Section: Popular Slam Approaches Available On Rosmentioning

confidence: 99%

RTAB‐Map as an open‐source lidar and visual simultaneous localization and mapping library for large‐scale and long‐term online operation

Labbé

Michaud

2018

Journal of Field Robotics

675

325

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“…In [37], the paper presented autonomous USAR robots from the perspective of achievements in research and technical aspects. The proposed solution attempted to handle two aspects of the SAR problem: First, for survivor search and detection, the system needs to be as close as possible to the survivors so they can be detected, even if the sensor only covers a very small portion of survivor s body.…”

Section: Literature Reviewmentioning

confidence: 99%

LSAR: Multi-UAV Collaboration for Search and Rescue Missions

2019

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In this paper, we consider the use of a team of multiple unmanned aerial vehicles (UAVs) to accomplish a search and rescue (SAR) mission in the minimum time possible while saving the maximum number of people. A novel technique for the SAR problem is proposed and referred to as the layered search and rescue (LSAR) algorithm. The novelty of LSAR involves simulating real disasters to distribute SAR tasks among UAVs. The performance of LSAR is compared, in terms of percentage of rescued survivors and rescue and execution times, with the max-sum, auction-based, and locust-inspired approaches for multi UAV task allocation (LIAM) and opportunistic task allocation (OTA) schemes. The simulation results show that the UAVs running the LSAR algorithm on average rescue approximately 74% of the survivors, which is 8% higher than the next best algorithm (LIAM). Moreover, this percentage increases with the number of UAVs, almost linearly with the least slope, which means more scalability and coverage is obtained in comparison to other algorithms. In addition, the empirical cumulative distribution function of LSAR results shows that the percentages of rescued survivors clustered around the [78%-100%] range under an exponential curve, meaning most results are above 50%. In comparison, all the other algorithms have almost equal distributions of their percentage of rescued survivor results. Furthermore, because the LSAR algorithm focuses on the center of the disaster, it finds more survivors and rescues them faster than the other algorithms, with an average of 55%∼77%. Moreover, most registered times to rescue survivors by LSAR are bounded by a time of 04:50:02 with 95% confidence for a one-month mission time.INDEX TERMS Autonomous agents, drones, search and rescue, unmanned aerial vehicles.

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“…Unmanned aerial vehicles (UAV) have the characteristics of low operating cost, good maneuverability, and no risk of casualties [1,2]. Accordingly, it has been applied to many fields, such as surveying and mapping [3], surveillance [4], bathymetry [5][6][7][8][9], and disaster rescue [10,11]. As a typical application, area coverage with UAVs has attracted great attention in robotics.…”

Section: Introductionmentioning

confidence: 99%

Multi-UAV Coverage through Two-Step Auction in Dynamic Environments

Sun

Tan

Yan

et al. 2022

Drones

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The cooperation of multiple unmanned aerial vehicles (Multi-UAV) can effectively solve the area coverage problem. However, developing an online multi-UAV coverage approach remains a challenge due to energy constraints and environmental dynamics. In this paper, we design a comprehensive framework for area coverage with multiple energy-limited UAVs in dynamic environments, which we call MCTA (Multi-UAV Coverage through Two-step Auction). Specifically, the online two-step auction mechanism is proposed to select the optimal action. Then, an obstacle avoidance mechanism is designed by defining several heuristic rules. After that, considering energy constraints, we develop the reverse auction mechanism to balance workload between multiple UAVs. Comprehensive experiments demonstrate that MCTA can achieve a high coverage rate while ensuring a low repeated coverage rate and average step deviation in most circumstances.

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Towards Highly Reliable Autonomy for Urban Search and Rescue Robots

Cited by 13 publications

References 13 publications

RTAB‐Map as an open‐source lidar and visual simultaneous localization and mapping library for large‐scale and long‐term online operation

RTAB‐Map as an open‐source lidar and visual simultaneous localization and mapping library for large‐scale and long‐term online operation

LSAR: Multi-UAV Collaboration for Search and Rescue Missions

Multi-UAV Coverage through Two-Step Auction in Dynamic Environments

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