Curating Long-Term Vector Maps

Autonomous service mobile robots need to consistently, accurately, and robustly localize in human environments despite changes to such environments over time. Episodic non-Markov Localization addresses the challenge of localization in such changing environments by classifying observations as arising from Long-Term, Short-Term, or Dynamic Features. However, in order to do so, EnML relies on an estimate of the Long-Term Vector Map (LTVM) that does not change over time. In this paper, we introduce a recursive algorithm to build and update the LTVM over time by reasoning about visibility constraints of objects observed over multiple robot deployments. We use a signed distance function (SDF) to filter out observations of short-term and dynamic features from multiple deployments of the robot. The remaining long-term observations are used to build a vector map by robust local linear regression. The uncertainty in the resulting LTVM is computed via Monte Carlo resampling the observations arising from long-term features. By combining occupancy-grid based SDF filtering of observations with continuous space regression of the filtered observations, our proposed approach builds, updates, and amends LTVMs over time, reasoning about all observations from all robot deployments in an environment. We present experimental results demonstrating the accuracy, robustness, and compact nature of the extracted LTVMs from several long-term robot datasets.

Authors

• Samer B. Nashed
• Joydeep Biswas

Keywords

• Robots
• Uncertainty
• Robustness
• Feature extraction
• Heuristic algorithms
• Monte Carlo methods
• Laser noise
• Vector Map
• Dynamic Characteristics
• Human Environment
• Mobile Robot
• Long-term Characteristics
• Signed Distance Function
• Center Of Mass
• Cost Function
• Uncertainty Estimation
• Nonlinear Least Squares
• Line Segment
• Current Function
• Set Of Lines
• Covariance Estimation
• Scattering Matrix
• Update Function
• Laser Ranging
• Inliers
• Bicubic Interpolation
• Occupancy Grid
• Latest State
• Weight Of Pixel