New optimization techniques for point feature and general curve feature based SLAM
- Publication Type:
- Thesis
- Issue Date:
- 2013
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This doctoral thesis deals with the feature based Simultaneous Localization and Mapping
(SLAM) problem. SLAM as defined in this thesis is the process of concurrently building
up a map of the environment and using this map to obtain improved estimates of the
location of the robot. In feature based SLAM, the robot relies on its ability to extract
useful navigation information from the data returned by its sensors. The robot typically
starts at an unknown location without priori knowledge of feature locations. From relative
observations of features and relative pose measurements, estimates of entire robot trajectory
and feature locations can be derived. Thus, the solution to SLAM problem enables
an autonomous vehicle navigates in a unknown environment autonomously. The advantage
of eliminating the need for artificial infrastructures or a priori topological knowledge
of the environment makes SLAM problem one of the hot research topics in the robotics
literature. Solution to the SLAM problem would be of inestimable value in a range of
applications such as exploration, surveillance, transportation, mining etc.
The critical problems for feature based SLAM implementations are as follows: 1) Because
SLAM problems are high dimensional, nonlinear and non-convex, when solving
SLAM problems, robust optimization techniques are required. 2) When the environment
is complex and unstructured, appropriate parametrization method is required to represent
environments with minimum information loss. 3) As robot navigates in the environment,
the information acquired by the onboard sensor increases. It is essential to develop
computationally tractable SLAM algorithms especially for general curve features.
This thesis presents the following contributions to feature based SLAM. First, a convex
optimization based approach for point feature SLAM problems is developed. Using the
proposed method, a unique solution can be obtained without any initial state estimates.
It will be shown that, the unique SDP solution obtained from the proposed method is very
close to the true solution to the SLAM problem. Second, a general curve feature based
SLAM formulation is presented. Instead of scattered points, in this formulation, the
environment is represented by a number of continuous curves. Using the new formulation, all
the available information from the sensor is utilized in the optimization process. Third,
method for converting curve feature to point feature is presented. Using the conversion
method, the curve feature SLAM problem can be transferred to point feature SLAM problem
and can be solved by the convex optimization based approach.
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