Slamprone

Slamprone is a term used in robotics and computer vision to describe systems that are highly susceptible to errors in Simultaneous Localization and Mapping (SLAM) under certain conditions. A slamprone system may produce significant localization drift, mapping artifacts, or loop-closure failures in challenging environments, particularly when features are sparse or dynamic objects are present.

Etymology and usage: The term combines SLAM with prone to indicate vulnerability to SLAM-related failures. It appears in robotics literature and workshop discussions since the 2010s as researchers discuss robustness requirements for mobile robots operating in real-world settings.

Characteristics: Common indicators include rapid drift during exploration, inconsistent map scale, and reliance on odometry without adequate recalibration. Slamprone behavior is especially pronounced in environments with low visual texture, repetitive patterns, or moving objects, where data association and feature tracking become unreliable.

Causes: Contributing factors include sparse or ambiguous features, poor sensor calibration, adverse lighting or weather conditions, dynamic scenes, and limited computational resources that hamper real-time optimization and loop-closure detection.

Mitigation and research directions: Approaches to reduce slamprone behavior include robust SLAM back-ends with strong loop-closure and outlier rejection, sensor fusion (visual-inertial or LiDAR), dynamic object filtering, and improved data association. Benchmarking and evaluation protocols that stress tests across varying feature density and dynamics are used to assess and compare slamproneness of different systems.

See also: SLAM, Visual Odometry, Localization, Mapping, Robotics.