Method

Multi-VLO System with Sanity Checker [ComboVLO]


Submitted on 8 Feb. 2021 10:19 by
Andrzej REINKE (UNIVERSITY OF BONN)

Running time:1 s
Environment:16 cores @ >3.5 Ghz (C/C++)

Method Description:
The odometry algorithms used in this paper are
generalized iterative closest point, point-to-
plane iterative closest point, normal
distributions transform (NDT), Color iterative
closest point, and Huang's method for combining
LiDAR and camera data.
Moreover, we also employ the so-called constant
velocity model (CVM), which directly uses the
estimation from the last timestamp without using
the sensor data.
A set of dynamics and kinematic sanity checks
that filter out potential estimation failures,
and a point cloud Chamfer distance-based
criterion, which chooses the most promising pose
estimation at each timestamp, independently from
the history.
Parameters:
For sanity checker we use l = 1.0m a_{\text{max}}
= 6.0 m/s^2 and v_th = 0.8 m/s, the search radius
r_s = 0.5m, and the number of scans used to build
the local map is N_map = 10.

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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