Addressing Data Annotation Challenges in Multiple Sensors: A Solution
  for Scania Collected Datasets
Ajinkya Khoche Aron Asefaw Alejandro Gonzalez Bogdan Timus Sina Sharif Mansouri Patric Jensfelt
Data annotation in autonomous vehicles is a critical step in the development
of Deep Neural Network (DNN) based models or the performance evaluation of the
perception system. This often takes the form of adding 3D bounding boxes on
time-sequential and registered series of point-sets captured from active
sensors like Light Detection and Ranging (LiDAR) and Radio Detection and
Ranging (RADAR). When annotating multiple active sensors, there is a need to
motion compensate and translate the points to a consistent coordinate frame and
timestamp respectively. However, highly dynamic objects pose a unique
challenge, as they can appear at different timestamps in each sensor's data.
Without knowing the speed of the objects, their position appears to be
different in different sensor outputs. Thus, even after motion compensation,
highly dynamic objects are not matched from multiple sensors in the same frame,
and human annotators struggle to add unique bounding boxes that capture all
objects. This article focuses on addressing this challenge, primarily within
the context of Scania collected datasets. The proposed solution takes a track
of an annotated object as input and uses the Moving Horizon Estimation (MHE) to
robustly estimate its speed. The estimated speed profile is utilized to correct
the position of the annotated box and add boxes to object clusters missed by
the original annotation.