1. Adaptive Hyper-Parameter Tuning
for Black-box LiDAR Odometry
Kenji Koide, Masashi Yokozuka, Shuji Oishi, and Atsuhiko Banno
National Institute of Advanced Industrial Science and Technology (AIST), Japan

2. Odometry Estimation
LiDAR Odometry Visual Odometry
Engel et al., Direct Sparse Odometry
Pan et al., MULLS: Versatile LiDAR SLAM via Multi-metric
Linear Least Square

3. Tuning is important
Odometry estimation/SLAM frameworks involve
many hyper-parameters
(e.g., downsample resolution, map resolution, keyframe interval...)
Many parameters need to be tuned depending on the sensor and environment
(e.g., Indoor/Outdoor, Mechanical Rotating/Solid-State LiDAR)
w/o parameter tuning
Estimation quality largely depends on the choice
of the parameters

4. Tuning is difficult
https://google-cartographer-ros.readthedocs.io/en/latest/tuning.html
Google Cartographer Tuning Guide says:
"Tuning Cartographer is unfortunately really difficult.
The system has many parameters many of which affect
each other."
MULLS, SOTA LiDAR SLAM framework, involves over 80 params
It's well documented, but you still need to understand in detail
how it works
https://github.com/YuePanEdward/MULLS
Some other frameworks don't even provide documentation...
Odometry estimation methods are surprisingly complex, parameter tuning is difficult

5. Automatic and adaptive parameter selection
for black-box LiDAR odometry
Indoor
Outdoor
Forest
Adaptive
Parameter
Selection
Environment descriptor
Param Set A
Param Set B
Param Set C
LiDAR
Odometry
Accuracy improvement by parameter selection
No knowledge on the inner working
Data-driven meta-algorithm as a potential
improvement for any odometry estimation methods

6. Data-driven black-box LiDAR odometry analysis

7. Offline parameter-error function modeling
Surrogate function for error prediction
Params Env. descriptor Odometry error
Data-driven function modeling
1. Sample a random parameter set
2. Run LiDAR odometry algorithm
3. For each sub-trajectory:
• Extract an environment descriptor
• Evaluate the odometry error (RTE)
4. Repeat 1~3
5. Fit a KNN regressor s.t.
Sequential Model-based Optimization
SMBO finds the param that maximizes the
expected improvement (EI):

8. Environment descriptor
NDT voxel histogram-based descriptor
1. Calc normal distribution voxels
M. Magnusson et. al, “Appearance-based loop detection from
3D laser data using the normal distributions transform,” ICRA2009
3. Create histogram and apply PCA (N=10)
The framework is agnostic to the descriptor; other hand-crafted as well as learned features can be used
2. Classify voxels into linear/planar/sphere
𝑒𝑖𝑔 Σ = 𝜆1, 𝜆2, 𝜆3 𝜆1 > 𝜆2 > 𝜆3
𝑁0
𝐿
, 𝑁0
𝑃
, 𝑁0
𝑆
𝑁1
𝐿
, 𝑁1
𝑃
, 𝑁1
𝑆
𝑁2
𝐿
, 𝑁2
𝑃
, 𝑁2
𝑆

9. Online parameter selection
Params Env. descriptor Odometry error
Surrogate function (KNN regressor)
Best parameter set for the current environment
1. Extract the descriptor for the current input cloud
2. Find the parameter set that minimizes the predicted error
𝑆 is nonlinear and non-convex run SMBO on 𝑺
Parameter selection is performed every second
①
②
③

10. Simple toy example
Simulated environment
(A) cave, (B) open space, (C) outdoor street
Odometry estimation algorithm
Keyframe-based NDT odometry with 2 params
- NDT resolution
- Keyframe interval
Need to be tuned depending on the environment
NDT resolution
Keyframe interval
Large Small
Better convergence Better accuracy
Small odometry drift Better stability
Parameter
Accuracy vs stability trade-off
Parameter settings
(1) Manually tuned (2) Fixed param (3) Adaptive param
256 offline SMBO trials

11. Simple toy example
Parameters are selected depending on the environment
without detailed knowledge of the algorithm
A meta tuning algorithm that can potentially improve
the accuracy of any odometry estimation methods

12. Evaluation on KITTI odometry estimation dataset
Geiger et. al, “Vision meets
Robotics: The KITTI dataset”,
IJRR2013
Odometry estimation algorithms
- Keyframe-based GICP odometry
- LeGO-LOAM [Tixiao, IROS2018]
- SuMa [Behley, RSS2018]
Three algorithms with totally different architectures
Parameter settings
(1) Manually tuned (2) Fixed param (3) Adaptive param
256 offline SMBO trials
For seq. 00
Training/validation set
Seq. 00-05 : for training
Seq. 06-10 : for validation

13. Sampled parameters and corresponding errors of GICP
Point location: sampled parameter set
Point color: odometry estimation error
Different sequences require different parameters
Max
corresponding
distance
Keyframe interval
- Seq. 00 requires a large max correspondence distance
to prevent estimation corruption

14. Sampled parameters and corresponding errors of GICP
Point location: sampled parameter set
Point color: odometry estimation error
Different sequences require different parameters
- Seq. 00 requires a large max correspondence distance
to prevent estimation corruption
- The best keyframe interval largely varies depending on
the environment
Max
corresponding
distance
Keyframe interval

15. Sampled parameters and corresponding errors of GICP
Point location: sampled parameter set
Point color: odometry estimation error
Different sequences require different parameters
- Seq. 00 requires a large max correspondence distance
to prevent estimation corruption
- The best keyframe interval largely varies depending on
the environment
Max
corresponding
distance
Keyframe interval
There is no parameter set that works well for all the seqs
A conservative param for seq. 00 Deteriorated accuracy
Param set for another seq Estimation corruption
Params must be adaptively tuned depending on the environment

16. Evaluation on KITTI odometry estimation dataset
Fixed parameter set : Improved accuracy on the training set Deteriorated accuracy on the test set
Adaptive parameter set : Improved accuracy on both the training and test sets

17. Conclusions
• An adaptive parameter tuning framework for black-box LiDAR odometry
is proposed
• The proposed framework uses a data-driven surrogate function modeling
for error prediction
• Offline parameter sampling and online parameter selection are efficiently
done with SMBO (Sequential Model-based Optimization)
• The proposed framework successfully improved the accuracy of different
algorithms in a practical situation