scholarly journals Sensor Fusion of Monocular Cameras and Laser Rangefinders for Line-Based Simultaneous Localization and Mapping (SLAM) Tasks in Autonomous Mobile Robots

Sensors ◽  
2012 ◽  
Vol 12 (1) ◽  
pp. 429-452 ◽  
Author(s):  
Xinzheng Zhang ◽  
Ahmad B. Rad ◽  
Yiu-Kwong Wong
Keyword(s):  
Mobile Robots ◽  
Sensor Fusion ◽  
Simultaneous Localization And Mapping ◽  
Autonomous Mobile Robots ◽  
Localization And Mapping

TSLAM: Tethered simultaneous localization and mapping for mobile robots

2017 ◽  
Vol 36 (12) ◽  
pp. 1363-1386 ◽  
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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