Selection of fusion operations using rank-score diversity for robot mapping and localization

Abstract

"Selection of fusion operations using rank-score diversity for robot mapping and localization" (2007). Faculty Publications. 24.

Multi-Layer Combinatorial Fusion Using Cognitive Diversity

“…Combinatorial fusion analysis [18], using multiple scoring systems and the RSC function, was subsequently developed and shown to be useful in a variety of domains [18,19,20]. These include text categorisation [21] and ranked versions thereof, protein structure prediction [13,22], information retrieval [1,23], target tracking and robotics [24,25], motif detection [26], CHIP-seq peak detection [27], visual informatics [28,29], cognitive neuroscience [30], virtual screening and drug discovery [8,9,31], deep learning [3], microarray analysis [32,33,34], data fusion [1,7], and portfolio management [35,36].…”

“…d) CFA provides combinatorial fusion with (2 t − t − 1 combinations for t original scoring systems) for any combination methods, which are efficient and effective in different domain applications in protein-structure predictions [13], stress identification [20], text categorisation [21], target tracking, robot mapping, and localisation [24,25], identification of degenerated motif [26], CHIP-Seq peak detection [27], combining visual cognitive systems [28,29], preference detection using eye movement [30], virtual screening [8], microarray gene expression [32,33,34], portfolio management [35,36], classifier ensemble [63,64], and online learning [71]. 2) MCF is based on the Kemeny rank space H n which has the following characteristics: a) It is a natural extension from the symmetric group S n and the bubble-sort Cayley graph B n [43,73].…”

Evaluation of a Parallel Architecture and Algorithm for Mapping and Localization

“…Localization is the process of determining the robot's location with respect to this map. A number of algorithmic approaches have been developed for acquiring and fusing sensor data (e.g., [2][6] [9]) and for estimating the map and robot location (e.g., [1][5] [16] [17]). In general, these approaches require the accumulation of large amounts of sensory data and the repeated integration of this data with a map model.…”