End-to-End 3D Object Detection using LiDAR Point Cloud
Gaurav Raut Advait Patole
There has been significant progress made in the field of autonomous vehicles.
Object detection and tracking are the primary tasks for any autonomous vehicle.
The task of object detection in autonomous vehicles relies on a variety of
sensors like cameras, and LiDAR. Although image features are typically
preferred, numerous approaches take spatial data as input. Exploiting this
information we present an approach wherein, using a novel encoding of the LiDAR
point cloud we infer the location of different classes near the autonomous
vehicles. This approach does not implement a bird's eye view approach, which is
generally applied for this application and thus saves the extensive
pre-processing required. After studying the numerous networks and approaches
used to solve this approach, we have implemented a novel model with the
intention to inculcate their advantages and avoid their shortcomings. The
output is predictions about the location and orientation of objects in the
scene in form of 3D bounding boxes and labels of scene objects.