russtedrake PRO
Roboticist at MIT and TRI
Geometric perception
(part 1)
MIT 6.800/6.843:
Robotic Manipulation
Fall 2021, Lecture 5
Follow live at https://slides.com/d/WiwXxtM/live
(or later at https://slides.com/russtedrake/fall21-lec05)
So far, we have assumed a "perception oracle" that could tell us \({}^WX^O\).
\}
Stop using "cheat ports"
Use the cameras instead!
Geometry sensors -- lidar / time of flight
Velodyne spinning lidar
Hokoyu scanning lidar
Luminar
(500m range)
Geometry sensors -- stereo
Carnegie multisense stereo
Point Grey Bumblebee
Geometry sensors -- structured light
Microsoft Kinect
Asus Xtion
https://arxiv.org/abs/1505.05459
Geometry sensors -- time of flight
Microsoft Kinect v2
Samsung Galaxy DepthVision
Geometry sensors -- projected texture stereo
Intel Realsense D415
Our pick for the "Manipulation Station"
Major advantage over e.g. ToF: multiple cameras don't interfere with each other.
(also iPhone TrueDepth)
from the docs: "Each pixel in the output image from depth_image is a 16bit unsigned short in millimeters."
two on wrist
Real point clouds are also messy
figure from Chris Sweeney et al. ICRA 2019.
to the board...
Lecture 5: Geometric Perception (part 1)
By russtedrake
Lecture 5: Geometric Perception (part 1)
MIT Robotic Manipulation Fall 2020 http://manipulation.csail.mit.edu
