Mapping

Mapping is the ability of a robot to understand its surroundings with external sensors such as LIDAR and camera. Robots have to recognize the position and shape of obstacles to avoid them. In mapping, grid mapping and machine learning algorithms are widely used[31][18]. Fig.3 shows mapping simulation results using grid mapping with 2D ray casting and 2D object clustering with k-means algorithm.

Contents

• Gaussian grid map
• Normal Distance Transform (NDT) map
  • Normal Distribution
  • Normal Distance Transform mapping steps
  • API
• Ray casting grid map
• Lidar to grid map
• Point cloud Sampling
  • Voxel Point Sampling
  • Farthest Point Sampling
  • Poisson Disk Sampling
• k-means object clustering
• Object shape recognition using circle fitting
• Object shape recognition using rectangle fitting
  • Step1: Adaptive range segmentation
  • Step2: Rectangle search
  • API
  • References
• Normal vector estimation
  • Normal vector calculation of a 3D triangle
  • Normal vector estimation with RANdam SAmpling Consensus(RANSAC)
• Distance Map
  • Example
  • API
  • References