Building large-scale, globally consistent maps is a challenging problem, made more difficult in environments with limited access, sparse features, or when using data collected by novice users. For such scenarios, where state-of-the-art mapping algorithms produce globally inconsistent maps, we introduce a systematic approach to incorporating sparse human corrections, which we term Human-in-the-Loop Simultaneous Localization and Mapping (HitL-SLAM). Given an initial factor graph for pose graph SLAM, HitL-SLAM accepts approximate, potentially erroneous, and rank-deficient human input, infers the intended correction via expectation maximization (EM), back-propagates the extracted corrections over the pose graph, and finally jointly optimizes the factor graph including the human inputs as human correction factor terms, to yield globally consistent large-scale maps. We thus contribute an EM formulation for inferring potentially rank-deficient human corrections to mapping, and human correction factor extensions to the factor graphs for pose graph SLAM that result in a principled approach to joint optimization of the pose graph while simultaneously accounting for multiple forms of human correction. We present empirical results showing the effectiveness of HitL-SLAM at generating globally accurate and consistent maps even when given poor initial estimates of the map.
Solutions to robotic mapping and SLAM have improved dramatically in recent years, but state-of-the-art algorithms still fall short at being able to repeatable and robustly produce globally consistent maps, particularly when deployed over large areas and by non-expert users. This is in part due to the difficulty of the data association problem @cite_2 @cite_0 @cite_8 . The idea of humans and robots collaborating in the process of map building to overcome such limitations is not new, and is known as Human-Augmented Mapping (HAM).
@cite_0 @cite_8 @cite_2
2127578024 207116529 2122455045
This paper discusses the recursive Bayesian formulation of the simultaneous localization and mapping (SLAM) problem in which probability distributions or estimates of absolute or relative locations of landmarks and vehicle pose are obtained. The paper focuses on three key areas: computational complexity; data association; and environment representation This paper surveys the most recent published techniques in the field of Simultaneous Localization and Mapping (SLAM). In particular it is focused on the existing techniques available to speed up the process, with the purpose to handel large scale scenarios. The main research field we plan to investigate is the filtering algorithms as a way of reducing the amount of data. It seems that almost all the current approaches can not perform consistent maps for large areas, mainly due to the increase of the computational cost and due to the uncertainties that become prohibitive when the scenario becomes larger. Simultaneous Localization and Mapping (SLAM) problem has been an active area of research in robotics for more than a decade. Many fundamental and practical aspects of SLAM have been addressed and some impressive practical solutions have been demonstrated. The aim of this paper is to provide a review of the current state of the research on feature based SLAM, in particular to examine the current understanding of the fundamental properties of the SLAM problem and associated issues with the view to consolidate recent achievements.