Simultaneous Localization and Mapping Using a Novel Dual Quaternion Particle Filter
Simultaneous Localization and Mapping Using a Novel Dual Quaternion Particle Filter
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DOI：
10.23919/icif.2018.8455347
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发表时间：
2018-07
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影响因子：
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通讯作者：
Kailai Li;G. Kurz;Lukas Bernreiter;U. Hanebeck
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文献类型：
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作者：
Kailai Li;G. Kurz;Lukas Bernreiter;U. Hanebeck
在本文中，我们提出了一种新颖的方法，使用低成本范围和陀螺仪传感器数据，基于双四元数粒子的随机滤波，对平面运动执行同步定位和建图（SLAM）。这里，SE(2)状态由单位对偶四元数表示，并进一步通过方向统计的分布进行随机建模，以便可以通过随机采样生成粒子。为了构建完整的 SLAM 系统，针对跟踪块提出了一种基于 Rao-Blackwellization 的新型双四元数粒子滤波器，并进一步与占用网格映射块集成。与之前提出的滤波方法不同，我们的方法可以在未知环境中存在多模态噪声的情况下执行跟踪，同时给出合理的映射结果。使用带有机载超声波传感器和 IMU 传感器的步行机器人在模拟和现实场景中的未知环境中导航，对该方法进行了进一步评估。
In this paper, we present a novel approach to perform simultaneous localization and mapping (SLAM) for planar motions based on stochastic filtering with dual quaternion particles using low-cost range and gyro sensor data. Here, SE(2) states are represented by unit dual quaternions and further get stochastically modeled by a distribution from directional statistics such that particles can be generated by random sampling. To build the full SLAM system, a novel dual quaternion particle filter based on Rao-Blackwellization is proposed for the tracking block, which is further integrated with an occupancy grid mapping block. Unlike previously proposed filtering approaches, our method can perform tracking in the presence of multi-modal noise in unknown environments while giving reasonable mapping results. The approach is further evaluated using a walking robot with on-board ultrasonic sensors and an IMU sensor navigating in an unknown environment in both simulated and real-world scenarios.