Light detection and ranging (LiDAR) is one of the enabling technologies for advanced
driver assistance and autonomy. Advances in solid-state photon detector arrays offer
the potential of high-performance LiDAR systems but require novel signal processing
approaches to fully exploit the dramatic increase in data volume an arrayed detector
can provide.
This thesis presents two approaches applicable to arrayed solid-state LiDAR. First, a
novel block independent sparse depth reconstruction framework is developed, which
utilises a random and very sparse illumination scheme to reduce illumination density while improving sampling times, which further remain constant for any array
size. Compressive sensing (CS) principles are used to reconstruct depth information
from small measurement subsets. The smaller problem size of blocks reduces the
reconstruction complexity, improves compressive depth reconstruction performance
and enables fast concurrent processing. A feasibility study of a system proposal for
this approach demonstrates that the required logic could be practically implemented
within detector size constraints. Second, a novel deep learning architecture called
LiDARNet is presented to localise surface returns from LiDAR waveforms with high
throughput. This single data driven processing approach can unify a wide range
of scenarios, making use of a training-by-simulation methodology. This augments
real datasets with challenging simulated conditions such as multiple returns and
high noise variance, while enabling rapid prototyping of fast data driven processing
approaches for arrayed LiDAR systems.
Both approaches are fast and practical processing methodologies for arrayed LiDAR
systems. These retrieve depth information with excellent depth resolution for wide
operating ranges, and are demonstrated on real and simulated data. LiDARNet is
a rapid approach to determine surface locations from LiDAR waveforms for efficient point cloud generation, while block sparse depth reconstruction is an efficient method to facilitate high-resolution depth maps at high frame rates with reduced power and memory requirements.Engineering and Physical Sciences Research Council (EPSRC
