# Go Straight, Turn Right:

Pose Graph Reduction through Trajectory Segmentation using Line Segments

Yasir Latif and Jose Neira

Pose Graph Reduction through Trajectory Segmentation using Line Segments

Yasir Latif and Jose Neira

- SLAM is a mature problem
- Solved (NO!)
- Least Square formulation

- Current Challenges
- Do it Faster!
- Robust SLAM

- Where does speed up come from?
- Sparse structure of the problem
- Architecture specific instructions (SSE2/SSE3)
- Problem specific data-structures (SLAM++)
- Very good open-source libraries for SLAM (iSAM/g2o/GTSAM etc)

- SLAM over days, weeks, years, decades(?)
- Increasing amount of data to deal with
- other problems (dynamic objects/loop closing)

- Sensor dependent map / Information sampling
- When to add new information to the Pose Graph?
- Are all robot poses equally informative?
- How much information is needed to generate a good map estimate?

- Go Straight, Turn Right
- Turns are interesting (more informative)
- Going straight is boring

- SLAM in general is a non-linear problem
- unless you are travelling along a straight line

- Local information is reliable
- local errors are small but accumulate over time and cause drifts
- local information is correct

- Reduce the pose-graph by discarding poses that lie (approximately) on a line
- (x,y,t) : A line segment starting at (x,y) extending in the direction t
- As long as the motion is in this direction, ignore the incoming sensor data
- when sufficient deviation has occurred, introduce new poses
- What is sufficient deviation?
- Which new poses get added?

For every new pose being added Update line to end at this pose make sure that all poses are with a distance Dmax if not: find the pose (i) with the largest distance terminate line at pose (i) keep the two poses (start,end) current start at pose (i+1), current end at this pose

- Computationally inexpensive
- works with just local information
- compares poses since start of last line
- online operation

- Great reduction in number of poses
- Bicocca: ~ 86% (at 5cm)
- New College: ~ 76% (at 5cm)

- Benefits
- ~10x speedup compared to full graph optimization
- Nearly as accurate (RMSE error 3 cm)
- Consistent Uncertainity Estimates

- Resource limited devices (Raspberry Pi/Cell phones)
- Computing the initial guess
- General speed up