Active Rendezvous for Multi-Robot Pose Graph Optimization using Sensing over Wi-Fi
Active Rendezvous for Multi-Robot Pose Graph Optimization using Sensing over Wi-Fi
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DOI：
10.1007/978-3-030-95459-8_51
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发表时间：
2019-07
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影响因子：
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通讯作者：
Weiying Wang;Ninad Jadhav;P. Vohs;Nathan Hughes;Mark Mazumder;Stephanie Gil
中科院分区：
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作者：
Weiying Wang;Ninad Jadhav;P. Vohs;Nathan Hughes;Mark Mazumder;Stephanie Gil
我们提出了一个新的框架之间的机器人团队performingPose Graph Optimization（PGO），解决了两个重要的挑战，多机器人SLAM：i），使信息交换“按需”viaActive Renewables不使用地图或机器人的位置，和ii）拒绝离群测量。我们的关键见解是利用机器人之间的通信信道中存在的相对位置数据来提高PGO的地面实况精度。我们开发了一个算法和实验框架integratingChannel State Information（CSI）与多机器人PGO;它是分布式的，适用于传统传感器经常失败的低光照或无特征环境。我们提出了广泛的实验结果，实际的机器人和观察usingActive Renewables结果在地面实况姿态误差减少64%，使用CSI的意见，以帮助离群拒绝地面实况姿态误差减少32%。这些结果显示了集成通信作为SLAM的新型传感器的潜力。
We present a novel framework for collaboration amongst a team of robots performingPose Graph Optimization(PGO) that addresses two important challenges for multi-robot SLAM: i) that of enabling information exchange “on-demand” viaActive Rendezvouswithout using a map or the robot’s location, and ii) that of rejecting outlying measurements. Our key insight is to exploit relative position data present in the communication channel between robots to improve groundtruth accuracy of PGO. We develop an algorithmic and experimental framework for integratingChannel State Information(CSI) with multi-robot PGO; it is distributed, and applicable in low-lighting or featureless environments where traditional sensors often fail. We present extensive experimental results on actual robots and observe that usingActive Rendezvousresults in a 64% reduction in ground truth pose error and that using CSI observations to aid outlier rejection reduces ground truth pose error by 32%. These results show the potential of integrating communication as a novel sensor for SLAM.