Syed Riaz un Nabi Jafri

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This paper presents a multi mobile robot simultaneous localization and mapping (SLAM) system for feature based environment learning by using team of exploring robots. The environmental information is measured through the dynamic sensor network in the shape of moving robots with unknown initial poses. Each robot is equipped with 2D laser scanner and a webcam(More)
This paper presents multi-robot simultaneous localization and mapping (SLAM) framework for a team of robots with unknown initial poses. The proposed solution is using feature based Rao-Blackwellised particle filter (RBPF) SLAM for each robot working in an unknown environment equipped only with 2D range sensor and communication module. To represent the(More)
This paper presents a new approach allowing to a group of robots team to merge their individual maps without any a priori knowledge about their relative positions. While moving, each robot acquires laser data and a video stream. The first data are used to create individual maps (SLAM), while images sequences are used to derive invariant visual descriptions(More)
This paper presents a new approach to solve multi robot simultaneous localization and mapping (SLAM) problem by allowing fast and efficient features exchange among teammates. Each mate is able to build its own SLAM solution by using feature based Rao-Blackwellised Particle Filter algorithm. This scheme proposes that every feature extracted by individual(More)
Vehicular movements are bounded to some rules and regulations based on the environment and road networks. Visual signals are the most common way of transferring perception of motion to immediate vehicles but these indications do not portray correct perception of the motion. Visual indications are reserved for forward vehicle intensions but such indications(More)
A Low cost Navigation Solution has developed for Unmanned Land vehicle (ULV) by using GPS & one ABS Sensor. Two IR Modules are integrated to detect obstacles in the path of movement. This combined arrangement is simple and gave satisfactory results at smooth surfaces. At rough surfaces results are very poor and vehicle lost its calculated path due(More)
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