2d-slam Laser Slam: Comparison of open source code Hectorslam gmapping Kartoslam Coreslam Lagoslam

Recently found a paper comparison of the current Ros under the 2D laser Slam open source code effect comparison:

For more information, see article: An evaluation of 2D SLAM techniques available in robot operating system

1. Algorithm Introduction

A. Hectorslam
Scan-matching (Gaussian-newton equation) + High sensor requirements

Requirements: High update frequency small measurement noise of the laser scanner. There is no need for mileage meter, so that the air drone and the ground car in the uneven areas of operation there is the possibility of application.

The laser beam lattice is optimized using the obtained map to estimate the representation of the laser point in the map and the probability of occupying the grid.

Among them, the scanning match is solved by the method of Gauss-Newton. Find the rigid body transform (X,y,theta) that maps the laser point set to the existing map.

(The matching method of contact also has the nearest neighbor matching method (ICP), and the scanmatcher part of the gmapping code is selected in two ways.) )

In order to avoid local minimum rather than global optimal (similar to the multi-peak model, the local gradient is the smallest, but not the global optimal) appears, the map uses the multi-resolution form.

the state estimation in navigation can be added by inertial measurement and EKF filtering.

B. gmapping
Proposed by Grisetti et al. and is a rao-blackwellized PF SLAM approach.
Adaptive resampling Technique

At present, the laser 2dslam is the most widely used method, Gmapping adopts the method of RBPF. It is important to understand the method of particle filtering, which uses statistical properties to describe the results of physical expressions.

Particle filter method generally requires a large number of particles to obtain good results, but this will inevitably introduce computational complexity, particle is a process based on the observation of gradual updating of the weight and convergence process, the resampling process is bound to the particle dissipation problem (depletion problem), the power of heavy particles significantly, Small weight particles will disappear (possibly the correct particle simulations may have a small weight in the middle stage and disappear).

Adaptive resampling technology is introduced to reduce the particle dissipation problem, and the calculation of particle distribution is not only dependent on the motion of the Robot (odometer), but also takes into account the current observation, which reduces the uncertainty of the robot position in the particle filtering step. (Fast-slam 2.0 of the thought, can be appropriately reduced number of particles)

C. Kartoslam
graph-based SLAM approach developed by SRI International ' s Karto robotics
Highly-optimized and non iterative Cholesky matrix decomposition for sparse linear systems as its solver
The Sparse Pose adjustment (SPA) is responsible for both scan matching and loop-closure procedures

Karto Open Libraries 2.0 SDK (Karto Open Libraries 2.0 is available under the LGPL Open Source license. You can try the full Karto SDK 2.1 for.) later under detailed study (compare below Mrpt library)

the core idea of graph optimization I think is mainly sparse flowers and least squares of matrices. See Graph Slam study: G2O

　　Kartoslam is a method based on graph optimization, which uses highly optimized and non-iterative Cholesky matrices to decouple sparse systems as solutions. The graph optimization method uses the mean value of the graph to represent the map, each node represents a location point of the robot trajectory and a sensor measurement data set, the pointing connection of the arrow indicates the movement of the position point of the continuous robot, each new node is added, and the map is updated according to the constraint of the node arrows in the space.

Kartoslam's Ros version, which uses sparse point adjustments (the Spare Pose adjustment (SPA)) is associated with scan matching and closed-loop detection. The more landmark, the greater the memory requirements, but the graph optimization method is more advantageous in the larger environment than other methods. In some cases kartoslam is more effective because he contains only a point figure (robot pose), and then asks for a map after the position is obtained.

D. Coreslam
Tinyslam Algorithm:two different steps (distance calculation and update of the map
Simple and easy

A slam algorithm that is simple and easy to understand to minimize the loss of performance. The algorithm is simplified as the two process of distance calculation and map updating, the first step, each scan input, based on the simple particle filter algorithm to calculate the distance, particle filter of the matching device for laser matching with the map, Each filter particle represents the possible position of the robot and the corresponding probability weights, all of which depend on the previous iterative calculations. Choose the best hypothesis distribution, that is, the low-weight particles disappear and the new particles are generated. In the update step, the scanned line is added to the map, and when the barrier appears, the set of adjustment points is drawn around the barrier point, rather than just one orphan point.

E. Lagoslam
Linear Approximation for Graph optimization
The optimization process requires no initial guess

The basic Graph Optimization Slam method is to utilize the minimization nonlinear non-convex cost function. Each iteration solves the initial problem of local convex approximation to update the diagram configuration, and the process iterates a certain number of times until the local minimum cost function is reached.  (assuming that the starting point has been iterated several times to minimize the local cost function).  Lagoslam is a linear approximate graph optimization, and no initial assumptions are required. Optimizer's method can have three options tree-based netork Optimizer (TORO), G2o,lago

2. Comparison of experimental results

The algorithm is compared in the case of the size simulation environment and the actual environment and CPU consumption respectively. Cartoslam and gampping a big advantage

Description: Limited capacity, speak of a problem welcome correction, for a while to this, after a specific look at the corresponding algorithm detailed paper introduction, there are problems to change ...

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2d-slam Laser Slam: Comparison of open source code Hectorslam gmapping Kartoslam Coreslam Lagoslam