DeepAI AI Chat
Log In Sign Up

MSL-RAPTOR: A 6DoF Relative Pose Tracker for Onboard Robotic Perception

12/16/2020
by   Benjamin Ramtoula, et al.
0

Determining the relative position and orientation of objects in an environment is a fundamental building block for a wide range of robotics applications. To accomplish this task efficiently in practical settings, a method must be fast, use common sensors, and generalize easily to new objects and environments. We present MSL-RAPTOR, a two-stage algorithm for tracking a rigid body with a monocular camera. The image is first processed by an efficient neural network-based front-end to detect new objects and track 2D bounding boxes between frames. The class label and bounding box is passed to the back-end that updates the object's pose using an unscented Kalman filter (UKF). The measurement posterior is fed back to the 2D tracker to improve robustness. The object's class is identified so a class-specific UKF can be used if custom dynamics and constraints are known. Adapting to track the pose of new classes only requires providing a trained 2D object detector or labeled 2D bounding box data, as well as the approximate size of the objects. The performance of MSL-RAPTOR is first verified on the NOCS-REAL275 dataset, achieving results comparable to RGB-D approaches despite not using depth measurements. When tracking a flying drone from onboard another drone, it outperforms the fastest comparable method in speed by a factor of 3, while giving lower translation and rotation median errors by 66 respectively.

READ FULL TEXT

page 1

page 2

page 3

page 4

04/09/2019

BoLTVOS: Box-Level Tracking for Video Object Segmentation

We approach video object segmentation (VOS) by splitting the task into t...
01/27/2019

6D Object Pose Estimation Based on 2D Bounding Box

In this paper, we present a simple but powerful method to tackle the pro...
08/23/2021

Marine vessel tracking using a monocular camera

In this paper, a new technique for camera calibration using only GPS dat...
10/23/2022

DMODE: Differential Monocular Object Distance Estimation Module without Class Specific Information

Using a single camera to estimate the distances of objects reduces costs...
06/24/2019

Pose Estimation for Non-Cooperative Rendezvous Using Neural Networks

This work introduces the Spacecraft Pose Network (SPN) for on-board esti...
03/02/2021

Depth from Camera Motion and Object Detection

This paper addresses the problem of learning to estimate the depth of de...
06/08/2022

Adaptive Neural Network-based Unscented Kalman Filter for Spacecraft Pose Tracking at Rendezvous

This paper presents a neural network-based Unscented Kalman Filter (UKF)...