Multisensor fusion

Parent: SLAM Index ,  geometric-metric-slam

Source: [Cometlabs What You Need to Know About SLAM](cometlabs what you-need-to-know-about-slam.md)

• Avoid limitations of using only one sensor
  • Relative measurements: provide precise positioning information constantly
  • At certain times absolute measurements are made to correct potential errors (correct drift)
• several approaches (for localisation), e.g.
  • merge sensor feeds at the lowest level before being processed homogeneously
  • hierarchical approaches (fuse state estimates derived independently from multiple sensors)
  • s. also  loose vs tight coupling
• combine pos measurements in a formal probabilistic framework (e.g. Markov Localisation Framework)
  • localisation problem consists of estimating the probability density over the space of all locations
  • MLF: combines info from sensors to form a combined belief in location

Source: [Wu 2018 Image-based camera localization: an overview](wu 2018-image-based-camera-localization_-an-overview.md)

• recently, vision and IMU fusion has attracted attention 
• universality and complementarity of visual-inertial sensors(s. why use the visual-inertial sensor combination? )
• main distinctions: loose vs tight coupling