Lidar Mapping

2021

thumbs MULLS: Versatile LiDAR SLAM via Multi-metric Linear Least Square.

  • Lidar only slam (it doesn’t addition information (e.g. imu) for undistortion.)
  • Use many kind of features (ground, facade, pillar, beam, etc) to make the system versatile. (and doesn’t depends on additional info for cloud, e.g ring).
  • Registration to a local submap. solve the new ICP problem based on TEASER (applying truncated least square estimation with semi-definite relaxation).
  • Process a loop closure and a pose graph optimization.

chrown0 BALM: Bundle adjustment for lidar mapping . But the main results shown for Livox Device (denser, small FOV).

  • Adaptive voxelization to match both plane and edge features without a segmentation.
  • Reduce to pose only BA.

chrown0 LiTAMIN2: Ultra Light LiDAR-based SLAM using Geometric Approximation applied with KL-Divergence. Using a covariance based ICP error, combined with a covariance shape error term (from KL divergence), which allow matching with very large voxel size, then making the registration extremely fast.

thumbs Range Image-based LiDAR Localization for Autonomous Vehicles.

  • Passion reconstruction get mesh. (small in memory)
  • Monte Carlo localization of lidar map. (particle filter with rendered range image)

unhappy LiLi-OM (LIvox LiDAR-Inertial Odometry and Mapping)

  • tightly-coupled idar-IMU hierarchical lide-window optimization.
  • factors : imu-preint, lidar plane/edge pairs, prior/marginalization terms.
  • conventional LiDARs : LOAM feature; solid-state LiDAR : use covariance.

chrown0 FAST-LIO (Fast LiDAR-Inertial Odometry) keyword : FAST

2020

chrown0 LIO-SAM Tightly-coupled Lidar Inertial Odometry via Smoothing and Mapping. In short, add imu pre-integration and sliding window to LOAM.

thumbs OverlapNet: Loop Closing for LiDAR-based SLAM. Top-down 2d view of lidar scan (with other info) for predict overlap rate and yaw.

chrown ISC (Intensity Scan Context) Coding Intensity and Geometry Relations for Loop Closure Detection. Encode lidar frame using geometry and intensity info (project intensity into ring distributed subspaces). Simple algorithm, and perform wonderfully in real data.

../_images/isc_test.png

2018

chrown0 LeGO-LOAM . Lightweight and Ground-Optimized Lidar Odometry and Mapping on Variable Terrain. See details and some comparisons.

2017

unhappy On the performance of metrics to predict quality in point cloud representations. Using absolute category rating (ACR) and able to perceive distortions.

unhappy On Subjective and Objective Quality Evaluation of Point Cloud Geometry. Point cloud quality metric using DSIS (double-stimulus impairement scale) methodology. Showing that current state-of-the-art objective metrics (point-to-point, point-to-plane or point-to-mesh) do not predict well visual quality, especially under typical distortions such as compression.

thumbs 2D SLAM Quality Evaluation Methods. The proportion of occupied and free cells (check wall blurry). The amount of corners and enclosed areas in a map.

2012

chrown OctoMap github project. Probabilistic representation, Modeling of unmapped areas, Efficiency (octree).

2009

thumbs On Measuring the Accuracy of SLAM Algorithms. (RPE vs APE) A metric that operates only on relative geometric relations between poses along the trajectory of the robot.