This paper deals with the task of developing a laboratory framework for testing simultaneous localization and mapping algorithms (SLAM). The laboratory framework is composed of three parts. The first one are sensors a...
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作者：
Ren, ZhengruSkjetne, Roger
Department of Marine Technology Norwegian University of Science and Technology NTNU TrondheimNO-7491 Norway
Department of Marine Technology Norwegian University of Science and Technology NTNU TrondheimNO-7491 Norway
Thruster-assisted position mooring (TAPM) is an attractive stationkeeping solution for long-term operation. Due to the complex environmental loads and system structure, increasing attention has been paid to improve th...
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In this work we introduced a robotic system to work in a sociable environment alongside humans. The robot is able to recognize information through sound and it is capable to speak in order to response or provide signi...
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Recently, there have been a number of variant simultaneouslocalization and mapping (SLAM) algorithms that have made substantial progress towards large-area scalability by parameterizing the SLAM posterior within the ...
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ISBN:
(纸本)0780389123
Recently, there have been a number of variant simultaneouslocalization and mapping (SLAM) algorithms that have made substantial progress towards large-area scalability by parameterizing the SLAM posterior within the information (canonical/inverse covariance) form. Of these, probably the most well-known and popular approach is the Sparse Extended Information Filter (SEIF) by Thrun et al. While SEIFs have been successfully implemented with a variety of challenging real-world datasets and have led to new insights into scalable SLAM, open research questions remain regarding the approximate sparsification procedure and its effect on map error consistency. In this paper, we examine the constant-time SEIF sparsification procedure in depth and offer new insight into issues of consistency. In particular, we show that exaggerated map inconsistency occurs within the global reference frame where estimation is performed, but that empirical testing shows that relative local map relationships are preserved. We then present a slightly modified version of their sparsification procedure, which is shown to preserve sparsity while also generating both local and global map estimates comparable to those obtained by the non-sparsified SLAM filter. While this modified approximation is no longer constant-time, it does serve as a theoretical benchmark against which to compare SEIFs constant-time results. We demonstrate our findings by benchmark comparison of the modified and original SEIF sparsification rule using simulation in the linear Gaussian SLAM case and real-world experiments for a nonlinear dataset.
Autonomous mobile machines use onboard sensors for navigation and obstacle avoidance. The accuracy of the sensor data in global frame is however dependent on the localization accuracy of the machine. simultaneous loca...
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ISBN:
(纸本)9783952426937
Autonomous mobile machines use onboard sensors for navigation and obstacle avoidance. The accuracy of the sensor data in global frame is however dependent on the localization accuracy of the machine. simultaneous localization and mapping algorithms (SLAM) are widely used with 3D laser scanners for mapping the world. They use scan matching algorithms to solve the accuracy problem by matching prior sensor data of the environment with the newly acquired data. However matching scans is not always possible. Insufficient amount of prior data or too few features in the scan can prevent the scan matching algorithm from finding a match. Thus it is important that also the mapping algorithm is tolerant to some degree of error in localization and calibration. We present a method for generating obstacle maps from smaller data segments at a time, thus making the mapping system more tolerant to navigation and calibration errors. The obstacle mapping method is tested with modified Avant multipurpose loader.
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