Simultaneous localization and mapping:
Simultaneous Location and Mapping (SLAM) is used in a computational problem that builds and updates the map of an unknown environment and simultaneously maintains the agent’s tracking location on location. It is used in computational geometry and robotics. It usually seems simple, but several algorithms are required to solve it. These algorithms solve it in a time that can be traced for some environments. Some approximate solution approaches consist of the extended Kalman filter, GraphSLAM, the particle filter, and the intersection of covariance. These algorithms apply to navigation, odometry for augmented reality and virtual reality, and robotic mapping. SLAM algorithms are used to tailor available resources to operational compliance. Therefore, the goal is never to achieve perfection. Autonomous vehicles, self-sufficient underwater vehicles, unmanned aerial vehicles, the latest home robots, and planetary rovers use published approaches.
SLAM problem:
Simultaneous mapping and localization are needed.
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For localization and mapping, the SLAM algorithms use the basic Chicken or Egg problems. The SLAM task includes mapping the environment and detecting the pose of the robot in relation to the environment. If the map is not available, the robot has difficulty locating itself. The location is required to build the map, which will help you find your location.
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To explore a static and unfamiliar environment by providing the robot controls and based on observations of nearby features, using SLAM, you can estimate the feature map, pose, or path of the robot.
Why is SLAM a difficult problem?
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There are several uncertainties, as there could be an observation error, pose error, accumulated error, and mapping error.
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The map and the path of the robot are unknown. Any errors in the robot’s path correspond to errors on the map.
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Observations and reference points are unknown in real-world mapping. Also, if the wrong data is selected, there could be catastrophic consequences. The error in the pose is correlated with the data associations.
FastSLAM algorithm:
The Flastlam algorithm uses the particle filter approach to the SLAM problem. Maintains a collection of particles. These particles included a map and the sampled robot path. The local Gaussian itself represents the features of the map. A separate set of Gaussian map features is created, constituting the map. The characteristics of the Gauss map are independent of the conditions.
How does the algorithm work?
First, the conditionally independent map features are given to the route. Factor one particle per path. This makes the map features independent. Then the correlation is removed. The new FastSLAM sample pose is updated and the observation characteristics are updated. This update can be done online. You can troubleshoot both online and offline based on SLAM. Instances include feature-based maps and grid-based algorithms.
FastSLAM 2.0 algorithm:
FastSLAM 2.0 sample poses are based on measurement and control to avoid the problem.
Step 1: Show off the new poses by extending the way back.
Step 2: Look at the features and update them.
Step 3: Re-sample.
Fast-SLAM Features:
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Each particle can trust itself. Supports decisions based on local data association.
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The data association decision is more robust and is based on a per-particle basis.
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It can provide a solution to SLAM problems online and offline.
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FastSLAM 1.0 is less efficient at creating samples. Nevertheless, FastSLAM 2.0 it is more and at the cost of mathematical complexity.