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SLAM stands for Simultaneous Localisation and Mapping. The map can be anything that associates locations with things that are at those locations. … This is not a SLAM problem anymore. Its just localisation. Wanting to know where you (or your agent) is within this map. …

PNG does not support float numbers (afaik). Hence the depth value needs to be encoded as a fixed point value. The fixed point was chosen, I guess, to get a good trade-off between accuracy (0.2 mm) and …

In SLAM frontends which use the Iterative Closest Point (ICP) algorithm for identifying the association between two matching point clouds, how can you determine if the algorithm is stuck in a local minimum …

As it says in the description of the file format, it is for graph based SLAM approaches. These work on minimizing the error of a pose constraint network. … Graph based pose graph optimizers are considered SLAM backends. How you generate the constraints e.g. from you range data is a front-end problem. There is a nice overview in these lecture notes. …

I don't recall a comprehensive SLAM survey article, though. …

I can't think of a reason why a velocity model (based on control commands) would be superior to an odometry model (which uses the actual wheel speeds). The lecture notes from Freiburg on motion mode …

SLAM is jointly estimating the sensor pose and a map, based on a sensor model and sometimes a model for the pose change. … SLAM can be part of the processing chain, but does not have to be. …

This marginalization is very popular in SLAM. The reason is that jointly sampling over position and map is impractical. …

From the wiki of the g2o file format: The information matrix or precision matrix which represent the uncertainty of the measurement error is the inverse of the covariance matrix. Hence, …

Instead of implementing a particle filter yourself, you could use a SLAM implementation e.g. from openslam. This should save you a lot of time, and will most likely give better results. …

You don't describe your setup in detail, and some of the units are missing, but my guess is that the $\sigma_\phi$ is mainly responsible for the initial error you are seeing. $\sin( 0.5 ) \times 30 …

One way you can use SLAM in your setup is to stop the robot every 30 cm or so, and perform a sweep with your lidar. You can then use e.g. one of the 2D SLAM packages from openslam. … SLAM works by associating features in one reading with the same feature in another reading. By having a single point reading you cannot tell if two readings have the same original scene point or not. …

This is the Simultaneous Localization and Mapping (SLAM) problem. It is still relative navigation. Now coming to your actual question on absolute navigation. … Although, as I said, SLAM is not an absolute method. The simplest form is direct landmark recognition. …

Measuring if something has moved is ok with just accelerometers, how much and where not. You may need to specify your application a little better, but it sounds like you should use an external trackin …

The text says: [...] where $N^T (\mu)$ linearly projects a 3D vector onto the 2D tangent space around the bearing vector $\mu$ [...]