SOF-SLAM: A Semantic Visual SLAM for Dynamic Environments

Towards Visual SLAM in Dynamic Environments

To build autonomous robots capable of operating wherever humans do, we must develop local­ ization and mapping strategies that can handle changing environments. Recent work by Se, Lowe and Little has shown that current machine vision technology makes visual SLAM feasible in a potentially wide range of static environments. However, their system treats vision system errors in a manner which impos...

SLAM Algorithms In Dynamic Environments

In this work the Kalman filter and the Particle filter are described and their performance in the Simaltaneous Localization And Mapping (SLAM) problem in static environments is discussed. Furthermore, this paper presents how derivatives of these filters are applied in order to solve the SLAM problem in dynamic environments. The Particle filter makes less assumptions about the probability distri...

Pop-up SLAM: a Semantic Monocular Plane SLAM for Low-texture Environments

Existing simultaneous localization and mapping (SLAM) algorithm is not robust in challenging low-texture environments because of few salient features. The resulting sparse or semi-dense map also conveys little information for motion planning. Though some work utilize plane or scene layout for dense map regularization, they require decent state estimation from other sources. In this paper, we pr...

The case for visual-inertial SLAM over visual SLAM

Featureless Visual Processing for SLAM in Changing Outdoor Environments

Vision-based SLAM is mostly a solved problem providing clear, sharp images can be obtained. However, in outdoor environments a number of factors such as rough terrain, high speeds and hardware limitations can result in these conditions not being met. High speed transit on rough terrain can lead to image blur and under/over exposure, problems that cannot easily be dealt with using low cost hardw...