Visual SLAM in dynamic environments based on object detection

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...

Object-based Visual SLAM: How Object Identity Informs Geometry

Objects are rich information sources about the environment. A 3D model of the objects, together with their semantic labels, can be used for camera localization as well as for cognitive reasoning about the environment. However, traditional frameworks for scene reconstruction usually map a cloud of points using structure-from-motion techniques, but do not provide objects representation. On the ot...

Online and Incremental Appearance-based SLAM in Highly Dynamic Environments

This paper presents a novel method for online and incremental appearance-based localization and mapping in a highly dynamic environment. Using position-invariant robust features (PIRFs), the method can achieve a high rate of recall with 100% precision. It can handle both strong perceptual aliasing and dynamic changes of places efficiently. Its performance also extends beyond conventional images...

Visual Trajectory Based SLAM