Biologically Inspired Mobile Robot Vision Localization

Abstract

Abstract-We present a robot localization system using biologically-inspired vision. Our system models two extensively studied human visual capabilities: (1) extracting the "gist" of a scene to produce a coarse localization hypothesis, and (2) refining it by locating salient landmark points in the scene. Gist is computed here as a holistic statistical signature of the image, yielding abstract scene classification and layout. Saliency is computed as a measure of interest at every image location, efficiently directing the time-consuming landmark identification process towards the most likely candidate locations in the image. The gist features and salient regions are then further processed using a Monte-Carlo localization algorithm to allow the robot to generate its position. We test the system in three different outdoor environments -building complex (38.4x54.86m area, 13966 testing images), vegetation-filled park (82.3x109.73m area, 26397 testing images), and open-field park (137.16x178.31m area, 34711 testing images) -each with its own challenges. The system is able to localize, on average, within 0.98, 2.63, and 3.46m, respectively, even with multiple kidnapped-robot instances.

Mobile robot trajectory tracking using noisy RSS measurements: An RFID approach

“…satisfies the Hamiltonian inequality (14) and, hence, guarantees the converge of the robot's trajectory towards its target.…”

“…The optimal feedback control gain K o can be determined by satisfying the Hamiltonian inequality (14). In other words, K is to be adaptively tuned to minimize the robot's tracking error.…”

“…This technique may be suitable for static structured indoor environments but not for dynamically varying ones. Besides trajectory tracking, some researchers contributed to the development of pose estimation paradigms using vision [12,13,14]. For such methods to perform well, the noise statistical model has to be known.…”

Biological Models for Active Vision: Towards a Unified Architecture

“…Recently, complete plausible models of the ventral (object recognition) pathway have appeared, based on the HMAX model [22]. Modern robotics has also embraced biological algorithms, with biologically-inspired SLAM algorithms [23,24], obstacle avoidance, and complete robotic architectures [25]. Attempts to build a comprehensive biologically plausible system have focussed on dynamic field-based models of different aspects of vision [26] and cognitive robots [25].…”

Monte Carlo Localization using 3D texture maps

“…There are two generalized methods for a mobile robot to perform localization in a given map: metric localization [1]- [3] and topological localization [4], [5]. Given a 2D map, the result of metric localization is a position (x, y) and heading θ in the map.…”