  3D object detection based on LiDAR point clouds is a crucial module in
autonomous driving particularly for long range sensing. Most of the research is
focused on achieving higher accuracy and these models are not optimized for
deployment on embedded systems from the perspective of latency and power
efficiency. For high speed driving scenarios, latency is a crucial parameter as
it provides more time to react to dangerous situations. Typically a voxel or
point-cloud based 3D convolution approach is utilized for this module. Firstly,
they are inefficient on embedded platforms as they are not suitable for
efficient parallelization. Secondly, they have a variable runtime due to level
of sparsity of the scene which is against the determinism needed in a safety
system. In this work, we aim to develop a very low latency algorithm with fixed
runtime. We propose a novel semantic segmentation architecture as a single
unified model for object center detection using key points, box predictions and
orientation prediction using binned classification in a simpler Bird's Eye View
(BEV) 2D representation. The proposed architecture can be trivially extended to
include semantic segmentation classes like road without any additional
computation. The proposed model has a latency of 4 ms on the embedded Nvidia
Xavier platform. The model is 5X faster than other top accuracy models with a
minimal accuracy degradation of 2% in Average Precision at IoU=0.5 on KITTI
dataset.
