Geometric perception

(part 1)

MIT 6.4210/2:

Robotic Manipulation

Fall 2022, Lecture 5

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(or later at

Basic Pick and Place

So far, we have assumed a "perception oracle" that could tell us \({}^WX^O\).


Stop using "cheat ports"

Use the cameras instead!

Pick and Place with Perception

Geometry sensors -- lidar / time of flight

Velodyne spinning lidar

Hokoyu scanning lidar


(500m range)

Geometry sensors -- stereo

Carnegie multisense stereo

Point Grey Bumblebee

Geometry sensors -- structured light

Microsoft Kinect

Asus Xtion

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 2022

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