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Some hints: Use Minskowski sums to "grow" the obstacles taking into account the shape/orientation of the robot. Not easy to understand and implement, BUT already implemented in geometrical algorithm libraries. After finding the path, postprocess the output. Try to use a smoother for the path, like voronoi diagram in order to go to the middle of the ...


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referring to your equation $$ \boldsymbol{a}=\boldsymbol{Rb}+\boldsymbol{t} ,$$ the position measurement would be $\boldsymbol{t}$ whilst the orientation measurement would be $\boldsymbol{R}$. These quantities can be obtained in different ways, e.g. position could be measured using GPS while orientation could be obtained using an IMU. I hope this helps, P....


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First thing to do is pick your World frame. This is your main frame where the 3D coordinates of your points and poses of your cameras will be. Typically the origin of this world frame is the pose of your first camera. You then triangulate every point with all of the cameras that see it. A rough guess can be computed with an algorithm like DLT, and then ...


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what you are looking for is written in the paper. Position refers only to x,y,z translational measurements while pose means position and orientation.


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I would somewhat disagree with the statement of lack of heterogenous systems. It is pretty common to always combine a camera sensor with an IMU. LIDAR + Camera has also been a pretty popular idea over the last few years. In regards to lack of more exotic sensors e.g Thermal, RADAR I would say it is due to the fact that the main purpose of SLAM is to build ...


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