Robust and Efficient Stereo Feature Tracking for Visual Odometry

Johnson, Andrew; Goldberg, Steve; Cheng, Yang; Matthies, Larry

doi:10.1109/robot.2008.4543184

Cited by 98 publications

(59 citation statements)

References 7 publications

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“…This is a commonly used approach in the literature (Mei et al, 2010;Nister et al, 2006), but an alternative technique is to detect keypoints in a single image, and then search for correspondences using dense stereo processing (Howard, 2008;Konolige et al, 2007). On the slow processors of the MERs, down-sampled image pyramids are used to speed up correlation (Johnson et al, 2008). In most VO systems, keypoint similarity is computed using a normalized cross correlation approach Konolige et al, 2007).…”

Section: Stereo Matchingmentioning

confidence: 99%

“…On the MERs, IMU and wheel odometry measurements are used to predict the attitude and position changes of the rover, respectively (Johnson et al, 2008). This approach has been upgraded for the MSL rover, where predictions are made using conservative motion bounds that account for potential wheel slip (Johnson et al, 2008).…”

Section: Keypoint Trackingmentioning

confidence: 99%

“…Thus, in the interests of covering maximal ground, the use of VO has been limited to short drives (less than 15 m) involving specific terrain and tasks, despite its high positional accuracy. While the upcoming Mars Science Lab (MSL) rover employs a faster 200 MHz processor, its effective driving speed will still be limited by the VO update cycle time (Johnson et al, 2008;Matthies et al, 2007).…”

Section: Using Sun Sensor and Inclinometer Measurements To Reduce Vo mentioning

confidence: 99%

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Visual odometry aided by a sun sensor and inclinometer

Lambert

Furgale

Barfoot

et al. 2011

2011 Aerospace Conference

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Section: Stereo Matchingmentioning

confidence: 99%

Section: Keypoint Trackingmentioning

confidence: 99%

Section: Using Sun Sensor and Inclinometer Measurements To Reduce Vo mentioning

confidence: 99%

See 1 more Smart Citation

Visual odometry aided by a sun sensor and inclinometer

Lambert

Furgale

Barfoot

et al. 2011

2011 Aerospace Conference

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“…The models obtained through stereo imagery are used for both automatic terrain assessment and visual odometry. Due to high computation load visual odometry is rarely used on the MERs, a more efficient algorithm was proposed for the Mars Science Laboratory mission planned for 2010 [12].…”

Section: Related Workmentioning

confidence: 99%

Autonomous planetary exploration using LIDAR data

Rekleitis

Bedwani

Dupuis

2009

2009 IEEE International Conference on Robotics and Automation

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Abstract-In this paper we present the approach for autonomous planetary exploration developed at the Canadian Space Agency. The goal of this work is to autonomously navigate to remote locations, well beyond the sensing horizon of the rover, with minimal interaction with a human operator. We employ LIDAR range sensors due to their accuracy, long range and robustness in the harsh lighting conditions of space. Irregular Triangular Meshes (ITMs) are used for representing the environment providing an accurate yet compact spatial representation. In this paper a novel path-planning technique through the ITM is introduced, which guides the rover through flatter terrain and safely away from obstacles. Experiments performed in CSA's Mars emulation terrain that validate our approach are also presented. I. INTRODUCTIONMobile robotics has enabled scientific breakthroughs in planetary exploration [1]. Recent accomplishments have demonstrated beyond doubt the necessity and feasibility of semi-autonomous rovers for conducting scientific exploration on other planets. Both Mars Exploration Rovers (MERs) "Spirit" and "Opportunity" have the ability to detect and avoid obstacles, picking a path that would take them along a safe trajectory. MER's have reached traverses of 300m/sol. On occasion, the rovers have had to travel to locations that were at the fringe of the horizon of their sensors or even slightly beyond.The next rover missions to Mars are the "Mars Science Laboratory" (MSL) [2] and ESA's ExoMars [3]. Both of these missions have set target traverse distances on the order of one kilometer per day. Both the MSL and ExoMars rovers are therefore expected to drive regularly a significant distance beyond the horizon of their environment sensors. Earthbased operators will therefore not know a-priori the detailed geometry of the environment and will thus not be able to select via-points for the rovers throughout their traverses.One of the key technologies that will be required is the ability to sense and model the 3D environment in which the rover has to navigate. To address the above mentioned issues, the Canadian Space Agency is developing a suite of technologies for long-range rover navigation. For the purposes of this paper, "long-range" is defined as a traverse that takes the rover beyond the horizon of the rover's environment sensors.In the next Section we discuss the state-of-the-art in robotic planetary exploration. Section II presents the overall process for planetary exploration together with a short description of our test-bed. Next we present a summary of our approach to environmental modelling, Section IV

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“…[8][9][10][11] One of the most popular approaches is visual odometry, which is defined as the incremental online estimation of robot motion from image sequences using an on-board camera. available at http://www.ual.es/personal/rgonzalez/videosVO.…”

Section: Introductionmentioning

confidence: 99%

Combined visual odometry and visual compass for off-road mobile robots localization

et al. 2011

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SUMMARYIn this paper, we present the work related to the application of a visual odometry approach to estimate the location of mobile robots operating in off-road conditions. The visual odometry approach is based on template matching, which deals with estimating the robot displacement through a matching process between two consecutive images. Standard visual odometry has been improved using visual compass method for orientation estimation. For this purpose, two consumer-grade monocular cameras have been employed. One camera is pointing at the ground under the robot, and the other is looking at the surrounding environment. Comparisons with popular localization approaches, through physical experiments in off-road conditions, have shown the satisfactory behavior of the proposed strategy.

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Robust and Efficient Stereo Feature Tracking for Visual Odometry

Cited by 98 publications

References 7 publications

Visual odometry aided by a sun sensor and inclinometer

Visual odometry aided by a sun sensor and inclinometer

Autonomous planetary exploration using LIDAR data

Combined visual odometry and visual compass for off-road mobile robots localization

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