4 accepted papers
Sparse feature-based maps provide a compact representation of the environment that admit efficient algorithms, for example simultaneous localization and mapping. These representations typically assume a static world and therefore contain static map features. However, since the world contains dynamic…
This paper reports on a model-assisted bundle adjustment framework in which visually-derived features are fused with an underlying three-dimensional (3D) mesh provided a priori. By using an approach inspired by the expectation-maximization (EM) class of algorithms, we introduce a hidden binary label
Feature learning has become popular in robotics due to recent advances in machine learning. In this paper, we propose a novel method to utilize the high-dimensional features from these techniques as observations in Bayesian estimation problems in a real-time manner. We develop an approach that: 1) p…
This paper reports on a 3D photomosaicing pipeline using data collected from an autonomous underwater vehicle performing simultaneous localization and mapping (SLAM). The pipeline projects and blends 2D imaging sonar data onto a large-scale 3D mesh that is either given a priori or derived from SLAM.…