Estimating uncertain spatial relationships in robotics
Autonomous robot vehicles
Globally Consistent Range Scan Alignment for Environment Mapping
Autonomous Robots
Fast, On-Line Learning of Globally Consistent Maps
Autonomous Robots
Navigation regimes for off-road autonomy
Navigation regimes for off-road autonomy
The TerraMax autonomous vehicle: Field Reports
Journal of Robotic Systems - Special Issue on the DARPA Grand Challenge, Part 2
Stanley: The robot that won the DARPA Grand Challenge: Research Articles
Journal of Robotic Systems - Special Issue on the DARPA Grand Challenge, Part 2
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Thin junction tree filters for simultaneous localization and mapping
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
A multilevel relaxation algorithm for simultaneous localization and mapping
IEEE Transactions on Robotics
Hierarchical SLAM: Real-Time Accurate Mapping of Large Environments
IEEE Transactions on Robotics
From neurons to robots: towards efficient biologically inspired filtering and SLAM
KI'10 Proceedings of the 33rd annual German conference on Advances in artificial intelligence
Large scale graph-based SLAM using aerial images as prior information
Autonomous Robots
Toward distributed declarative control of networked cyber-physical systems
UIC'10 Proceedings of the 7th international conference on Ubiquitous intelligence and computing
Mapping for the Support of First Responders in Critical Domains
Journal of Intelligent and Robotic Systems
A probabilistic framework for learning kinematic models of articulated objects
Journal of Artificial Intelligence Research
Laser and Radar Based Robotic Perception
Foundations and Trends in Robotics
Walk&Sketch: create floor plans with an RGB-D camera
Proceedings of the 2012 ACM Conference on Ubiquitous Computing
Graph optimization with unstructured covariance: fast, accurate, linear approximation
SIMPAR'12 Proceedings of the Third international conference on Simulation, Modeling, and Programming for Autonomous Robots
A novel loop closure detection method in monocular SLAM
Intelligent Service Robotics
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Learning models of the environment is one of the fundamental tasks of mobile robots since maps are needed for a wide range of robotic applications, such as navigation and transportation tasks, service robotic applications, and several others. In the past, numerous efficient approaches to map learning have been proposed. Most of them, however, assume that the robot lives on a plane. In this paper, we present a highly efficient maximum-likelihood approach that is able to solve 3-D and 2-D problems. Our approach addresses the so-called graph-based formulation of simultaneous localization and mapping (SLAM) and can be seen as an extension of Olson's algorithm toward non-flat environments. It applies a novel parameterization of the nodes of the graph that significantly improves the performance of the algorithm and can 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. Furthermore, our approach is able to appropriately distribute the roll, pitch, and yaw error over a sequence of poses in 3-D mapping problems. We implemented our technique and compared it with multiple other graph-based SLAM solutions. As we demonstrate in simulated and real-world experiments, our method converges faster than the other approaches and yields accurate maps of the environment.