April 18, 2023

Sparse Surface AdjustmentM. Ruhnke, R. Kümmerle, G. Grisetti, W. Burgard

Metric 3D Models

►Essential for tasks like:►Object recognition►Manipulation

Metric 3D Models

►Essential for tasks like:►Object recognition►Manipulation

►Key challenges in model

acquisition with mobile robots►Errors in pose estimate►Measurement errors

Model Creation

►Optimize the sensor poses►Registration / SLAM

►Reduce the impact of measurement errors►Use optimal sensor distance►Local noise reduction techniques (Moving Least

Squares, Statistical Outlier Removal, …)►Pose information is mostly not considered

►Sensor pose gives information about normal direction and range of the measurement

Sparse Surface Adjustment

► Goal: Jointly optimize robot poses and surface points positions

► Surface Model► Model of measurement uncertainties► Data association: find corresponding points ► Utilize sparse graph optimizer framework g2o

Surface Model

►Range measurements sample surfaces

►Assumption: Piecewise regular surfaces

►Surface sample►3D Position►covariance ►normal (local neighborhood)

►Range measurement►Sensor specific Covariance

►Dependent on range and incidence angle►Gaussian error distributions

Sensor Model

front view side view

~ 3.5m

~ 0.7m

Kinect RGB-D

Data Association

►Normal shooting as data association heuristic

►Assign surfaces samples of different observations

►Covariance►Large error weight in

direction of the normal ►Small weight for errors

in tangential direction

Optimization

►Iteratively:►Optimize system with g2o ►Re-compute:

►surface point characteristics (covariance, normals)►data association

SSA 2D: Intel Dataset

Object 5mm Resolution

AASS Loop Dataset*

SLAM result (input)

SSA result

*Courtesy of Martin Magnusson, AASS, Örebro, Sweden

SSA 3D: Example Cup

Example: Scan Refinement

►SSA refines scans based on more certain nearby measurements

after optimizationraw scan

Comparison SSA / MLS

►Moving Least Squares (MLS)►Local smoothing method►No correction of robot poses

►Sparse Surface Adjustment (SSA)►Robot pose correction & smoother surfaces

SSA result

MLS result

Summary SSA

►Iterative refinement of ►Sensor poses►Surface points positions

►Considering range & sensor dependent uncertainties

►Re-computation of data association►Uses PCL and FLANN