Robust Probabilistic Robot Arm Keypoint Detection Exploiting Kinematic Knowledge

Lukas Meyer, Leonard Klüpfel, Maximilian Durner, Rudolph Triebel

Keywords: Robot Keypoint Estimation, Monte Carlo Dropout, Deep Learning, Uncertainty Estimation

TL;DR: We detect 2D keypoints of robot arms and their corresponding uncertainty for pose estimation to correct erroneous kinematics.

Abstract:

We propose PK-ROKED, a novel probabilistic deep-learning algorithm to detect keypoints of a robotic manipulator in camera images and to robustly estimate the positioning inaccuracies w.r.t the camera frame.
Our algorithm uses monocular images as a primary input source and augments these with prior knowledge about the keypoint locations based on the robot’s forward kinematics.
As output, the network provides 2D image coordinates of the keypoints and an associated uncertainty measure, where the latter is obtained using Monte Carlo dropout.
In experiments on two different robotic systems, we show that our network provides superior detection results compared to the state-of-the-art.
We furthermore analyze the precision of different estimation approaches to obtain an uncertainty measure.