# Go Straight, Turn Right:

Pose Graph Reduction through Trajectory Segmentation using Line Segments

Yasir Latif and Jose Neira

### SLAM : A mature problem

• 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)

### Problem: Lifelong (t → ∞) SLAM

• 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?

### What is interesting?

• Go Straight, Turn Right
• 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

### Main Idea

• 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?

### Algorithm

```      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
```

### Results

• 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

### Where can it be used

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