A general consistent decentralized Simultaneous Localization And Mapping solution

Abstract

In this paper, we propose a new approach to the decentralized Simultaneous Localization And Mapping (SLAM) problem. The goal is to demonstrate the feasibility of decentralized localization using low-density maps built with low-cost sensors. This problem is challenging at different levels. Indeed, each vehicle localization tends to drift over time independently of one another making the global localization of a fleet hard to achieve. To counter this effect, called SLAM inconsistency and which has been stated numerous times in the literature, we introduce a model to represent the natural drift of SLAM algorithms. Its integration inside an Extended Kalman Filter is explained along with simulations validating its use. The second part of this paper presents the fusion architecture designed to solve the different problems arising in a decentralized scheme. It avoids data incest, which is an important source of inconsistency, and integrates the previously mentioned SLAM drift in the estimates produced. This architecture also separates the SLAM classically used for mono-vehicle applications from the high-level decentralized part offering flexibility regarding sensors and algorithms at a low-level. Other aspects, involved by the multi-vehicle settings, are also taken into account (communication losses, latencies, desynchronizations, unknown initial positions of the fleet members and data association). The whole algorithm has been tested in various scenarios with vehicles equipped with a single camera and an odometer. The results, from both simulated and real scenarios, show that our approach can work in real time with very small bandwidth requirements.

Simultaneous Localization and Mapping: A Survey of Current Trends in Autonomous Driving

“…It has been observed by multiple authors [111]. It has also been shown experimentally in [112] and [113]. In [114], the authors show that the 2D formulation of the SLAM problem is not convex and as such has local minima in which the optimization can be trapped.…”

“…The accuracy has not been measured. In [113], outdoor experiments with two or three vehicles are performed. The method relies on the exchange of visual landmarks.…”

Communicating Multi-UAV System for Cooperative SLAM-based Exploration

“…The exchanged information may be composed of key-frames and map points shared between a robot and a server [32], or only features of selected key-frames and relative-pose estimates shared among robots and ground station [13]. But mostly, robots exchange their local copies of the map and their poses [4,33,14]. The amount of exchanged data may rapidly increase in size, which may cause network congestion and data loss.…”

“…In this type of system, a synchronization among ROS cores is required to allow robot to robot communication. Hence, the multi-master fkie package 4 , where cores synchronization is ensured by UDP protocol, is used. The TCP protocol is responsible for exchanging data among ROS topics through the network.…”

Underwater Multi-Vehicle Trajectory Alignment and Mapping Using Acoustic and Optical Constraints

“…To overcome these limitations, the distributed SLAM algorithms were created. In distributed SLAM, each vehicle builds local maps and trajectories, and they devise strategies to keep these local approximations within a global coherence with respect to the maps and trajectories on the other vehicles using the passing of messages between vehicles [ 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 ]. Given limited communications underwater, distributed approaches are more likely to be useful for underwater applications.…”