russtedrake PRO
Roboticist at MIT and TRI
Geometric perception
(part 1)
MIT 6.881: Robotic Manipulation
Fall 2020, Lecture 6
Follow live at https://slides.com/russtedrake/fall20-lec06/live
(or later at https://slides.com/russtedrake/fall20-lec06)
So far, we have assumed a "perception oracle" that could tell us \(X^O_W\).
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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 "whiteboard"...
Lecture 6: Geometric Perception (part 1)
By russtedrake
Lecture 6: Geometric Perception (part 1)
MIT Robotic Manipulation Fall 2020 http://manipulation.csail.mit.edu
