SLAM Toolbox: SLAM for the dynamic world

BS-SLAM: Shaping the World

This paper presents BS-SLAM, a simultaneous localization and mapping algorithm for use in unstructured environments that is effective regardless of whether features correspond to simple geometric primitives such as lines or not. The coordinates of the control points defining a set of B-splines are used to form a complete and compact description of the environment, thus making it feasible to use...

Multi-resolution SLAM for Real World Navigation

In this paper a hierarchical multi-resolution approach allowing for high precision and distinctiveness is presented. The method combines topological and metric paradigm. The metric approach, based on the Kalman Filter, uses a new concept to avoid the problem of the drift in odometry. For the topological framework the fingerprint sequence approach is used. During the construction of the topologi...

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...

Spatiotemporal Articulated Models for Dynamic SLAM

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...

The case for visual-inertial SLAM over visual SLAM