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Autonomous driving has achieved rapid development over the last few decades,including the machine perception as an important issue of it. Although objectdetection based on conventional cameras has achieved remarkable results in2D/3D, non-visual sensors such as 3D LiDAR still have incomparable advantagesin the accuracy of object position detection. However, the challenge alsoexists with the difficulty in properly interpreting point cloud generated byLiDAR. This paper presents a multi-modal-based online learning system for 3DLiDAR-based object classification in urban environments, including cars,cyclists and pedestrians. The proposed system aims to effectively transfer themature detection capabilities based on visual sensors to the new model learningbased on non-visual sensors through a multi-target tracker (i.e. using onesensor to train another). In particular, it integrates the Online RandomForests (ORF) [1] method, which inherently has the abilities of fast andmulti-class learning. Through experiments, we show that our system is capableof learning a high-performance model for LiDAR-based 3D object classificationon-the-fly, which is especially suitable for robotics in-situ deployment whileresponding to the widespread challenge of insufficient detector generalizationcapabilities.

Efficient Online Transfer Learning for 3D Object Classification in Autonomous Driving