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Lukas Platinsky

3 accepted papers

2022

Quantity over Quality: Training an AV Motion Planner with Large Scale Commodity Vision Data

IROS 2022poster

With the Autonomous Vehicle (AV) industry shifting towards machine-learned approaches for motion plan-ning [1], the performance of self-driving systems is starting to rely heavily on large quantities of expert driving demon-strations. However, collecting this demonstration data typically involves ex…

Cited by 2Source ↗Scholar ↗
2021

What data do we need for training an AV motion planner?

ICRA 2021poster

We investigate what grade of sensor data is required for training an imitation-learning-based AV planner on human expert demonstration. Machine-learned planners [1] are very hungry for training data, which is usually collected using vehicles equipped with the same sensors used for autonomous operati…

Cited by 15Source ↗Scholar ↗
2017

Monocular visual odometry: Sparse joint optimisation or dense alternation?

ICRA 2017poster

Real-time monocular SLAM is increasingly mature and entering commercial products. However, there is a divide between two techniques providing similar performance. Despite the rise of ‘dense’ and ‘semi-dense’ methods which use large proportions of the pixels in a video stream to estimate motion and s…

Cited by 22Source ↗Scholar ↗