Robust Position Tracking for Mobile Robots with Adaptive Evolutionary Particle Filter

TSLAM: Tethered simultaneous localization and mapping for mobile robots

The International Journal of Robotics Research ◽

10.1177/0278364917732639 ◽

2017 ◽

Vol 36 (12) ◽

pp. 1363-1386 ◽

Cited By ~ 4

Author(s):

Patrick McGarey ◽

Kirk MacTavish ◽

François Pomerleau ◽

Timothy D Barfoot

Keyword(s):

Mobile Robots ◽

Particle Filter ◽

Low Cost ◽

Simultaneous Localization And Mapping ◽

Ground Truth ◽

Anchor Point ◽

Outlier Rejection ◽

Cluttered Environments ◽

Localization And Mapping ◽

Anchor Points

Tethered mobile robots are useful for exploration in steep, rugged, and dangerous terrain. A tether can provide a robot with robust communications, power, and mechanical support, but also constrains motion. In cluttered environments, the tether will wrap around a number of intermediate ‘anchor points’, complicating navigation. We show that by measuring the length of tether deployed and the bearing to the most recent anchor point, we can formulate a tethered simultaneous localization and mapping (TSLAM) problem that allows us to estimate the pose of the robot and the positions of the anchor points, using only low-cost, nonvisual sensors. This information is used by the robot to safely return along an outgoing trajectory while avoiding tether entanglement. We are motivated by TSLAM as a building block to aid conventional, camera, and laser-based approaches to simultaneous localization and mapping (SLAM), which tend to fail in dark and or dusty environments. Unlike conventional range-bearing SLAM, the TSLAM problem must account for the fact that the tether-length measurements are a function of the robot’s pose and all the intermediate anchor-point positions. While this fact has implications on the sparsity that can be exploited in our method, we show that a solution to the TSLAM problem can still be found and formulate two approaches: (i) an online particle filter based on FastSLAM and (ii) an efficient, offline batch solution. We demonstrate that either method outperforms odometry alone, both in simulation and in experiments using our TReX (Tethered Robotic eXplorer) mobile robot operating in flat-indoor and steep-outdoor environments. For the indoor experiment, we compare each method using the same dataset with ground truth, showing that batch TSLAM outperforms particle-filter TSLAM in localization and mapping accuracy, owing to superior anchor-point detection, data association, and outlier rejection.

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Real-time 3D pointing gesture recognition for mobile robots with cascade HMM and particle filter

Image and Vision Computing ◽

10.1016/j.imavis.2010.08.006 ◽

2011 ◽

Vol 29 (1) ◽

pp. 51-63 ◽

Cited By ~ 43

Author(s):

Chang-Beom Park ◽

Seong-Whan Lee

Keyword(s):

Mobile Robots ◽

Particle Filter ◽

Real Time ◽

Gesture Recognition ◽

Pointing Gesture

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Position tracking and recognition of everyday objects by using sensors embedded in an environment and mounted on mobile robots

2012 IEEE International Conference on Robotics and Automation ◽

10.1109/icra.2012.6225329 ◽

2012 ◽

Cited By ~ 6

Author(s):

Kouji Murakami ◽

Kazuya Matsuo ◽

Tsutomu Hasegawa ◽

Ryo Kurazume

Keyword(s):

Mobile Robots ◽

Position Tracking ◽

Everyday Objects ◽

Tracking And Recognition

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Self-adaptive Monte Carlo localization for mobile robots using range finders

Robotica ◽

10.1017/s0263574711000567 ◽

2011 ◽

Vol 30 (2) ◽

pp. 229-244 ◽

Cited By ~ 40

Author(s):

Lei Zhang ◽

René Zapata ◽

Pascal Lépinay

Keyword(s):

Monte Carlo ◽

Mobile Robots ◽

Position Tracking ◽

Localization Algorithm ◽

Global Localization ◽

Adaptive Monte Carlo ◽

Local Sample ◽

Monte Carlo Localization ◽

Sample Set ◽

Self Adaptive

SUMMARYIn order to achieve the autonomy of mobile robots, effective localization is a necessary prerequisite. In this paper, we propose an improved Monte Carlo localization algorithm using self-adaptive samples (abbreviated as SAMCL). By employing a pre-caching technique to reduce the online computational burden, SAMCL is more efficient than the regular MCL. Further, we define the concept of similar energy region (SER), which is a set of poses (grid cells) having similar energy with the robot in the robot space. By distributing global samples in SER instead of distributing randomly in the map, SAMCL obtains a better performance in localization. Position tracking, global localization and the kidnapped robot problem are the three sub-problems of the localization problem. Most localization approaches focus on solving one of these sub-problems. However, SAMCL solves all the three sub-problems together, thanks to self-adaptive samples that can automatically separate themselves into a global sample set and a local sample set according to needs. The validity and the efficiency of the SAMCL algorithm are demonstrated by both simulations and experiments carried out with different intentions. Extensive experimental results and comparisons are also given in this paper.

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