2 research outputs found
Moving object tracking employing rigid body motion on matrix Lie groups
In this paper we propose a novel method for estimating rigid body motion by
modeling the object state directly in the space of the rigid body motion group
SE(2). It has been recently observed that a noisy manoeuvring object in SE(2)
exhibits banana-shaped probability density contours in its pose. For this
reason, we propose and investigate two state space models for moving object
tracking: (i) a direct product SE(2)xR3 and (ii) a direct product of the two
rigid body motion groups SE(2)xSE(2). The first term within these two state
space constructions describes the current pose of the rigid body, while the
second one employs its second order dynamics, i.e., the velocities. By this, we
gain the flexibility of tracking omnidirectional motion in the vein of a
constant velocity model, but also accounting for the dynamics in the rotation
component. Since the SE(2) group is a matrix Lie group, we solve this problem
by using the extended Kalman filter on matrix Lie groups and provide a detailed
derivation of the proposed filters. We analyze the performance of the filters
on a large number of synthetic trajectories and compare them with (i) the
extended Kalman filter based constant velocity and turn rate model and (ii) the
linear Kalman filter based constant velocity model. The results show that the
proposed filters outperform the other two filters on a wide spectrum of types
of motion.Comment: 19th International Conference on Information Fusion (FUSION), Special
Session on Directional Estimatio
Stereo-based Multi-motion Visual Odometry for Mobile Robots
With the development of computer vision, visual odometry is adopted by more
and more mobile robots. However, we found that not only its own pose, but the
poses of other moving objects are also crucial for the decision of the robot.
In addition, the visual odometry will be greatly disturbed when a significant
moving object appears. In this letter, a stereo-based multi-motion visual
odometry method is proposed to acquire the poses of the robot and other moving
objects. In order to obtain the poses simultaneously, a continuous motion
segmentation module and a coordinate conversion module are applied to the
traditional visual odometry pipeline. As a result, poses of all moving objects
can be acquired and transformed into the ground coordinate system. The
experimental results show that the proposed multi-motion visual odometry can
effectively eliminate the influence of moving objects on the visual odometry,
as well as achieve 10 cm in position and 3{\deg} in orientation RMSE (Root Mean
Square Error) of each moving object.Comment: 5 pages, 5 figure