Method

Real-Time Appearance-Based Mapping [st] [lc] [RTAB-Map]
[Anonymous Submission]

Submitted on 25 Jul. 2018 15:00 by
[Anonymous Submission]

Running time:0.1 s
Environment:1 core @ 2.5 Ghz (C/C++)

Method Description:
RTAB-Map is a graph-based SLAM approach.
In this configuration, stereo images are used as input
and a Frame-to-Map (F2M) approach is used for
odometry. Feature tracking and motion estimation
are done against a feature map and poses are refined
with local bundle adjustment (g2o). Stereo correspondences
are computed using optical flow. Loop closure detection is
done with a bag-of-words approach and the graph is
optimized using GTSAM.
Parameters:
Version 0.17.4 built with g2o and gtsam dependencies
Custom parameters:
--Vis/MaxFeatures 1500
--RGBD/LinearUpdate 0
--RGBD/AngularUpdate 0
--RGBD/ProximityBySpace false
--Rtabmap/DetectionRate 2
--Kp/MaxFeatures 750
--Kp/FlannRebalancingFactor 1.0
--GFTT/QualityLevel 0.01
--GFTT/MinDistance 7
--OdomF2M/MaxSize 3000
--Rtabmap/CreateIntermediateNodes true
Latex Bibtex:

Detailed Results

From all test sequences (sequences 11-21), our benchmark computes translational and rotational errors for all possible subsequences of length (5,10,50,100,150,...,400) meters. Our evaluation ranks methods according to the average of those values, where errors are measured in percent (for translation) and in degrees per meter (for rotation). Details for different trajectory lengths and driving speeds can be found in the plots underneath. Furthermore, the first 5 test trajectories and error plots are shown below.

Test Set Average


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Sequence 11


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Sequence 12


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Sequence 13


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Sequence 14


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Sequence 15


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