Probabilistic Qualitative Localization and Mapping

Simultaneous localization and mapping (SLAM) is essential in numerous robotics applications such as autonomous navigation. Traditional SLAM approaches infer the metric state of the robot along with a metric map of the environment. While existing algorithms exhibit good results, they are still sensitive to measurement noise, sensors quality, data association and are still computationally expensive. Alternatively, we note that some navigation and mapping missions can be achieved using only qualitative geometric information, an approach known as qualitative spatial reasoning (QSR). In this work we contribute a novel probabilistic qualitative localization and mapping approach, which extends the state of the art by inferring also the qualitative state of the camera poses (localization), as well as incorporating probabilistic connections between views (in time and in space). Our method is in particular appealing in scenarios with a small number of salient landmarks and sparse landmark tracks. We evaluate our approach in simulation and in a real-world dataset, and show its superior performance and low complexity compared to state of the art.

Authors

• Roee Mor
• Vadim Indelman

Keywords

• Location awareness
• Simultaneous localization and mapping
• Probabilistic logic
• Cognition
• Planning
• Complexity theory
• Sensors
• Qualitative Mapping
• Qualitative Approach
• State Of The Art
• Low Complexity
• Mapping Approach
• Visuospatial
• Measurement Noise
• Simulation Approach
• Camera Pose
• Environment Map
• Autonomous Navigation
• Robot State
• Nonexpansive Mapping
• Number Of Landmarks
• Time Step
• Action Plan
• Measurement Model
• Line-of-sight
• Motion Model
• Landmark Localization
• Qualitative Estimation
• Robot Navigation
• Geometric Constraints
• Small Problems
• Number Of Hypotheses
• Local Frame
• Spatial Partitioning
• Fast Estimation
• Monocular Camera