Uncertainty Estimation and its Application in LiDAR-based Object Detection

The Hong Kong University of Science and Technology
Department of Computer Science and Engineering


PhD Thesis Defence


Title: "Uncertainty Estimation and its Application in LiDAR-based Object 
Detection"

By

Mr. Peng YUN


Abstract

Machine learning has attracted tremendous attention from researchers in 
various fields. In past decades, machine-learning techniques make 
remarkable progress and get great success across a variety of domains, 
such as robotics, computer vision, astronomy, biology, etc. One of the 
things that makes them so fascinating is that they often interact directly 
with the external world. However, the external world is rarely stable. 
Applying machine learning techniques in critical applications, like 
autonomous driving, requires not only point predictions but also reliable 
uncertainty measurements. The source of uncertainties in machine learning 
can be generally classified into three categories: data source, model 
parameters, and model structures.

In this thesis, we take probabilistic theory as theoretical foundations 
and adopt error propagation as well as Laplace approximation to estimate 
the uncertainty in various stages of a perceptual algorithm. We take 
LiDAR-based 3D object detection as an instance to demonstrate the 
feasibility and apply it in the context of autonomous driving. For input 
point clouds, we model the uncertainties in extrinsic parameters of a 
multi-homogeneous LiDAR system and propagate them into each point to 
improve the robustness of algorithms in geometric tasks. In weight space, 
we evaluate the posterior distribution of weight parameters in deep neural 
networks with Laplace approximation and adopt it as an uncertainty 
measurement for each parameter. They are further used to compute Bayesian 
constraints for preserving old-task knowledge along with knowledge 
distillation regularizers. In predictions, we tailor Laplace approximation 
methods to estimate epistemic uncertainty for 3D object detection. 
Compared to conventional point-estimation perception results, the 
prediction with epistemic uncertainty enriches the result representation 
and provides the reliability information for downstream tasks. In 
conclusion, this thesis demonstrates the feasibility of uncertainty 
estimation in various stages of deep neural networks and its application 
in multi-sensor fusion, incremental learning, and reliable predictions. 
Some promising future works are also discussed at the end of this thesis.


Date:			Monday, 21 November 2022

Time:			4:00pm - 6:00pm

Venue:			Room 5501
 			lifts 25/26

Chairperson:		Prof. Man Hoi WONG (ECE)

Committee Members:	Prof. Ming LIU (Supervisor)
 			Prof. Qifeng CHEN
 			Prof. Dit Yan YEUNG
 			Prof. Lujia WANG (ECE)
 			Prof. Hesheng WANG (Shanghai Jiao Tong University)


**** ALL are Welcome ****