A Tree Parameterization for Efficiently Computing Maximum Likelihood Maps using Gradient Descent
Year: 2007
Type: conference-paper
Abstract: In 2006, Olson et al. presented a novel approach to address the graph-based simultaneous localization and mapping problem by applying stochastic gradient descent to minimize the error introduced by constraints.Together with multi-level relaxation, this is one of the most robust and efficient maximum likelihood techniques published so far.In this paper, we present an extension of Olson's algorithm.It applies a novel parameterization of the nodes in the graph that significantly improves the performance and enables us to cope with arbitrary network topologies.The latter allows us to bound the complexity of the algorithm to the size of the mapped area and not to the length of the trajectory as it is the case with both previous approaches.We implemented our technique and compared it to multi-level relaxation and Olson's algorithm.As we demonstrate in simulated and in real world experiments, our approach converges faster than the other approaches and yields accurate maps of the environment.
Source: Robotics: Science and Systems
Authors: G. Grisetti, C. Stachniss, S. Grzonka, W. Burgard
Institution: University of Freiburg
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
Funder: Deutsche Forschungsgemeinschaft
Award: G719518300
OpenAlex ID: W2105018789