| dc.description.abstract | Simultaneous Localization and Mapping (SLAM) algorithms are used
by autonomous robots to build or update maps of an environment while
maintaining their position simultaneously. A fundamental open problem
in SLAM is the e ective representation of the map in unknown,
ambiguous, complex, dynamic environments. Representing such environments
in a suitable manner is a complex task. Existing approaches
to SLAM use map representations that store individual features (range
measurements, image patches, or higher level semantic features) and
their locations in the environment. The choice of how the map is represented
produces limitations which in many ways are unfavourable
for application in real-world scenarios. In this thesis, a new approach
to SLAM is explored that rede nes sensing and robot motion as acts
of deformation of a di erentiable surface. Distance elds and level set
methods are utilized to de ne a parallel to the components of the SLAM
estimation process and an algorithm is developed and demonstrated.
The variational framework developed is capable of representing complex
dynamic scenes and spatially varying uncertainty for sensor and
robot models. | en |
