One of the primary problems in marine robot navigation is the growth of uncertainty. Sensory measurements of the environment provide an enticing source of information about vehicle location. Various current approaches to AUV sensor data fusion fall short of incorporating environmental measurements in navigation estimation to improve navigation performance in unmapped environments. We present a ...

We propose an online spatiotemporal articulation model estimation framework that estimates both articulated structure as well as a temporal prediction model solely using passive observations. The resulting model can predict future motions of an articulated object with high confidence because of the spatial and temporal structure. We demonstrate the effectiveness of the predictive model by incor...

much attention has been increasingly paid to the technology of field and service robotics. The FSR conferences have been held bi-a collection of selected papers from FSR'03. To promote the robot industry, the potential needs and the market of robot technology have been investigated, and it has been predicted that field and service robots is the area most expected to enlarge the robot market rat...

In this work, we examine the classic problem of robot navigation via visual simultaneous localization and mapping (SLAM), but introducing the concept of dual optical and thermal (cross-spectral) sensing with the addition of sensor handover from one to the other. In our approach we use a novel combination of two primary sensors: co-registered optical and thermal cameras.Mobile robot navigation i...

We demonstrate the possibilities offered by the new NXT platform for CS artificial intelligence curricular development. The NXT provides a number of hardware improvements over the RCX, including a faster processor and more memory. We demonstrate the curricular materials developed in the LMICSE archive, including autonomous mobile robot localization and SLAM.

Robot navigation in poorly structured and uneven outdoor environments is an unsolved problem. Thus we present a SLAM (simultaneous localization and mapping) approach that is based on “leveled range scans”. The method is combining 3D perception with 2D localization and mapping. In this way established path planning and 2D navigation algorithms can be used in uneven terrain without the computatio...

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Robots often have to navigate robustly despite incomplete information about the terrain or their location in the terrain. In this case, they often use greedy methods to make planning tractable. In this paper, we analyze two such robot-navigation methods. The first method is Greedy Localization, which determines the location of a robot in known terrain by always moving it to the closest location...

Simultaneous localization and mapping (SLAM) algorithms are of vital importance in mobile robotics. This paper presents novel Augmented Reality (AR) visualization techniques for SLAM algorithms, with the purpose of assisting algorithm development. We identify important algorithm invariants and parameters and combine research in uncertainty visualization and AR, to develop novel AR visualization...

In this paper a new localization approach combining the metric and topological paradigm is presented. The main idea is to connect local metric maps by means of a global topological map. This allows a compact environment model which does not require global metric consistency and permits both precision and robustness. The method uses a 360 degree laser scanner in order to extract lines for the me...