  Autonomous driving has achieved rapid development over the last few decades,
including the machine perception as an important issue of it. Although object
detection based on conventional cameras has achieved remarkable results in
2D/3D, non-visual sensors such as 3D LiDAR still have incomparable advantages
in the accuracy of object position detection. However, the challenge also
exists with the difficulty in properly interpreting point cloud generated by
LiDAR. This paper presents a multi-modal-based online learning system for 3D
LiDAR-based object classification in urban environments, including cars,
cyclists and pedestrians. The proposed system aims to effectively transfer the
mature detection capabilities based on visual sensors to the new model learning
based on non-visual sensors through a multi-target tracker (i.e. using one
sensor to train another). In particular, it integrates the Online Random
Forests (ORF) [1] method, which inherently has the abilities of fast and
multi-class learning. Through experiments, we show that our system is capable
of learning a high-performance model for LiDAR-based 3D object classification
on-the-fly, which is especially suitable for robotics in-situ deployment while
responding to the widespread challenge of insufficient detector generalization
capabilities.

Robotics