Abstract:As researchers have pushed the limits of what can be accomplished by a single robot operating in a known or unknown environment, a greater emphasis has been placed on the utilization of mobile multi-robotic systems to accomplish various objectives. In transitioning from a robot-centric approach to a system-centric approach, considerations must be made for the computational and communicative aspects of the group as a whole, in addition to electromechanical considerations of individual robots. This paper reviews… Show more
“…Accelerometer data need to be integrated twice to yield the position, whereas rate-gyro data are only integrated once to track the orientation. Therefore, any small errors in the measurement of acceleration and angular velocity are integrated into large errors in velocity, which are compounded into still larger errors in position (Rone and Ben-Tzvi 2013; Wang et al 2014; Woodman 2007). The errors are cumulative and increase with time.…”
Section: Localization Sensors and Techniquesmentioning
confidence: 99%
“…The images contain a sufficient amount of meaningful information (color, texture, shape, etc.) to estimate the movement of a camera in a static environment (Rone and Ben-Tzvi 2013). Fig.…”
Accurate localization of a vehicle is a fundamental challenge and one of the most important tasks of mobile robots. For autonomous navigation, motion tracking, and obstacle detection and avoidance, a robot must maintain knowledge of its position over time. Vision-based odometry is a robust technique utilized for this purpose. It allows a vehicle to localize itself robustly by using only a stream of images captured by a camera attached to the vehicle. This paper presents a review of state-of-the-art visual odometry (VO) and its types, approaches, applications, and challenges. VO is compared with the most common localization sensors and techniques, such as inertial navigation systems, global positioning systems, and laser sensors. Several areas for future research are also highlighted.
“…Accelerometer data need to be integrated twice to yield the position, whereas rate-gyro data are only integrated once to track the orientation. Therefore, any small errors in the measurement of acceleration and angular velocity are integrated into large errors in velocity, which are compounded into still larger errors in position (Rone and Ben-Tzvi 2013; Wang et al 2014; Woodman 2007). The errors are cumulative and increase with time.…”
Section: Localization Sensors and Techniquesmentioning
confidence: 99%
“…The images contain a sufficient amount of meaningful information (color, texture, shape, etc.) to estimate the movement of a camera in a static environment (Rone and Ben-Tzvi 2013). Fig.…”
Accurate localization of a vehicle is a fundamental challenge and one of the most important tasks of mobile robots. For autonomous navigation, motion tracking, and obstacle detection and avoidance, a robot must maintain knowledge of its position over time. Vision-based odometry is a robust technique utilized for this purpose. It allows a vehicle to localize itself robustly by using only a stream of images captured by a camera attached to the vehicle. This paper presents a review of state-of-the-art visual odometry (VO) and its types, approaches, applications, and challenges. VO is compared with the most common localization sensors and techniques, such as inertial navigation systems, global positioning systems, and laser sensors. Several areas for future research are also highlighted.
“…If there is no encounter at the beginning of the sequence, then there is a degree of freedom on the second trajectory and it is necessary to add a prior to fix the second robot trajectory [12]. However, before the first encounter, there's no a prior good initial estimate.…”
Section: Pose Graphs Fusionmentioning
confidence: 99%
“…However, the calculation process is too complicated and it is hard to handle the closure circumstance [4]. Therefore, researchers gradually transferred from the traditional filter optimization algorithm to graph optimization algorithm [5].…”
“…Schmitt et al [19] developed a probabilistic state estimation method for multi-robot team localization based on vision sensors. Rone et al [20] demonstrated multi-robotic system with emphasis on mapping, localization and motion planning. Jeong et al [21] presented a localization method for multiple mobile robot systems using filtered directional RSS technique.…”
Intellectualization of life is a general tendency due to the proliferation of technology and science. Based on this concept, this paper presents multi-group localization algorithms and detection algorithms for multi-group service robot system (MGSR). Shopping cart problem is considered as an exemplary multi-group service robot system. The MGSR is designed to provide users with co-service by multiple carts and allows multiple users operation simultaneously. In MGSR, a cart carrying personal belongings of the user follows the user automatically and provides real-time position information to the user. To fulfill estimating the location of MGSR, hybrid external localization algorithm based on combination of QR location information and ZigBee location estimate is proposed. To detect and track a cart by another cart with LRF, we define cart features in LRF data and employ a support vector data description method. Recognition of usercart groups in MGSR is realized by ZigBee blind nodes on the cart. We verified the feasibility of the proposed algorithms for MGSR through three experiment trials.
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