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As the wikipedia page of Occupancy grid mapping explains, the result of the mapping process is a binary 1 or 0, occupied or not, the decision itself may be based on noisy data, which involves the probabilistic assessment of prior information to infer the posterior probability of the occupancy.


Cause of the intrinsic noise in sensory data, we have to consider a probabilistic model (mostly Gaussian) for the sensor measurements. As a matter of fact, the description and definition of the mapping problem will be probabilistic. The goal is to compute the most likely map given the sensor data and commands given to the robot: In occupancy grid mapping as ...


Actually, the HECTOR_NAVIGATION package depends on other packages and one of them is ceres_catkin ( which you need to clone and build in your catkin workspace. However, this ceres_catkin further depends on catkin_simple (, glog_catkin (


Probability and statistics. Stochastic signal processing. Estimation and Detection theory (I highly recommend that you find a class that uses Harry VanTrees's book and that offers office hours, that you enroll, and study, and that you reserve lots of time in your schedule to take it -- if you can learn that stuff by reading the book you're somewhere in the ...


The Probabilistic Robotics written by Thuran and his colleagues was completely helpful for me. Also, you can follow one of their colleague videos on youtube for SLAM.


The core part of the HectorSLAM is in the file hector_slam/hector_mapping/include/hector_slam_lib/matcher/ScanMatcher.h and hector_slam/hector_mapping/include/hector_slam_lib/map/OccGridMapUtil.h. The implementation of the scan matching in HectorSLAM uses the maximum likelihood estimator (MLE), which is implemented in the function estimateTransformationLogLh....

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