Existing LiDAR-based SLAM systems may be sensitive to noisy observations and uncertain pose predictions

The gap

Evidence profile

Sourced from the stated research gap and future-work section of the source papers, classified as general, spanning 3 journals. Those papers have been cited 7 times in total.

Supporting evidence — 4 representative gaps

• LiDAR–Visual–Inertial Multi-UGV Collaborative SLAM Framework (2026) · Drones · cited 2× · doi

  The lack of integration of LiDAR, visual, and inertial sensors in existing collaborative SLAM frameworks. The inability of existing C-SLAM methods to achieve robust and accurate global localization performance in real-world environments. The need for a framework that can effectively utilize the strengths of different sensors to achieve superior collaborative localization and mapping performance.

  generalstated research gap
  Keywords: lack integration lidar visual inertial sensors existing collaborative
• UAV–UGV Collaborative Localization in GNSS-Denied Large-Scale Environments: An Anchor-Free VIO–UWB Fusion with Adaptive Weighting and Outlier Suppression (2026) · Drones · cited 5× · doi

  The lack of global localization information such as GNSS in large-scale outdoor environments leads to accumulated global drift. Prior collaborative SLAM frameworks have limitations in generating positional loop closures between platforms after initialization. There is a need for a robust and accurate localization method for UAV-UGV teams in GNSS-denied environments.

  generalstated research gap
  Keywords: lack global localization information gnss large-scale outdoor environments
• UVP-LIO: Uncertainty-Aware Voxel-Plane Mapping for Robust LiDAR-Inertial Odometry (2026) · ISPRS International Journal of Geo-Information · doi

  Existing LiDAR-based SLAM systems may be sensitive to noisy observations and uncertain pose predictions. The paper identifies the need for a more robust LiDAR-inertial odometry method that can handle uncertain or motion-distorted observations. Current methods may not provide a consistent uncertainty representation for both registration and map maintenance.

  generalstated research gap
  Keywords: existing lidar-based slam systems sensitive noisy observations uncertain
• A survey of the Evolution of SLAM Techniques for Autonomous Robots (2016–2026) (2026) · Applied and Computational Engineering · doi

  The paper suggests that future research should focus on the enhancement of robustness, scalability, and adaptability of SLAM systems. The use of multi-sensor fusion methods and learning-based SLAM approaches is suggested. The paper also suggests that further research should be done on uncertainty estimation and optimizing for resource-constrained platforms.

  generalfuture-work sectionevidence 5/5
  Keywords: paper suggests future research focus enhancement robustness scalability

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