DeepAI AI Chat
Log In Sign Up

Detecting Invalid Map Merges in Lifelong SLAM

by   Matthias Holoch, et al.

For Lifelong SLAM, one has to deal with temporary localization failures, e.g., induced by kidnapping. We achieve this by starting a new map and merging it with the previous map as soon as relocalization succeeds. Since relocalization methods are fallible, it can happen that such a merge is invalid, e.g., due to perceptual aliasing. To address this issue, we propose methods to detect and undo invalid merges. These methods compare incoming scans with scans that were previously merged into the current map and consider how well they agree with each other. Evaluation of our methods takes place using a dataset that consists of multiple flat and office environments, as well as the public MIT Stata Center dataset. We show that methods based on a change detection algorithm and on comparison of gridmaps perform well in both environments and can be run in real-time with a reasonable computational cost.


page 3

page 6


Predicting Performance of SLAM Algorithms

Among the abilities that autonomous mobile robots should exhibit, map bu...

Dual-SLAM: A framework for robust single camera navigation

SLAM (Simultaneous Localization And Mapping) seeks to provide a moving a...

CAE-RLSM: Consistent and Efficient Redundant Line Segment Merging for Online Feature Map Building

In order to obtain a compact line segment-based map representation for l...

MAOMaps: A Photo-Realistic Benchmark For vSLAM and Map Merging Quality Assessment

Running numerous experiments in simulation is a necessary step before de...

Region Prediction for Efficient Robot Localization on Large Maps

Recognizing already explored places (a.k.a. place recognition) is a fund...

Fault-Diagnosing SLAM for Varying Scale Change Detection

In this paper, we present a new fault diagnosis (FD) -based approach for...

SLAM: SLO-Aware Memory Optimization for Serverless Applications

Serverless computing paradigm has become more ingrained into the industr...