Heading-aided odometry and range-data integration for positioning of autonomous mining vehicles

Bakambu, J.N.; Polotski, Vladimir; Cohen, Paul R.

doi:10.1109/cca.2000.897437

Cited by 16 publications

(8 citation statements)

References 4 publications

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“…Besides, the robot has often to negotiate steps in order to cross wastewater channels, and in this process the robot can lose grip, further distorting the wheel odometry to a great extent. Even though some devices can improve the odometry reliability [3], these errors cannot be ignored in the long term.…”

Section: Related Workmentioning

confidence: 99%

A Robust Localization System for Inspection Robots in Sewer Networks

Alejo

Caballero

Merino

2019

Sensors

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Sewers represent a very important infrastructure of cities whose state should be monitored periodically. However, the length of such infrastructure prevents sensor networks from being applicable. In this paper, we present a mobile platform (SIAR) designed to inspect the sewer network. It is capable of sensing gas concentrations and detecting failures in the network such as cracks and holes in the floor and walls or zones were the water is not flowing. These alarms should be precisely geo-localized to allow the operators performing the required correcting measures. To this end, this paper presents a robust localization system for global pose estimation on sewers. It makes use of prior information of the sewer network, including its topology, the different cross sections traversed and the position of some elements such as manholes. The system is based on a Monte Carlo Localization system that fuses wheel and RGB-D odometry for the prediction stage. The update step takes into account the sewer network topology for discarding wrong hypotheses. Additionally, the localization is further refined with novel updating steps proposed in this paper which are activated whenever a discrete element in the sewer network is detected or the relative orientation of the robot over the sewer gallery could be estimated. Each part of the system has been validated with real data obtained from the sewers of Barcelona. The whole system is able to obtain median localization errors in the order of one meter in all cases. Finally, the paper also includes comparisons with state-of-the-art Simultaneous Localization and Mapping (SLAM) systems that demonstrate the convenience of the approach.

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Section: Related Workmentioning

confidence: 99%

A Robust Localization System for Inspection Robots in Sewer Networks

Alejo

Caballero

Merino

2019

Sensors

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“…The gyro signal is compensated for bias and roll/pitch oscillations using the inclinometer data. More details about coupling odometry-gyro-inclinometer data for enhanced localization can be found in [7]. The second step involves correlation between the locally acquired range data and the available survey map, based upon the approach described in [3].…”

Section: A Platform Localizationmentioning

confidence: 99%

“…an opening appears on one side), the system switches from centerline to single-wallfollowing and reverts back to centerline following once the opening is passed. Appropriate controllers for skid-steering vehicles have been developed [5,7,8] adapting methods previously used for path following controller synthesis.…”

Section: B Wall-following Controllermentioning

confidence: 99%

Integrated Autonomous System for Exploration and Navigation in Underground Mines

Bakambu¹

2006

2006 IEEE/RSJ International Conference on Intelligent Robots and Systems

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This paper describes an autonomous platform for exploration and navigation within networks of tunnels, as those typically found in underground mines and caves. In this context, we propose a system allowing two modes of operation: exploration/surveying mode and autonomous navigation mode. In the exploration mode, a remotely located supervisor instructs the platform to move through successive sections of the network, gathering range data that is then concatenated into 2D/3D survey maps of the environment. In the navigation mode, the supervisor specifies high-level missions on the previously acquired survey maps. A motion planner then translates each mission into a set of consecutive navigation actions, separated by natural landmarks. Mission execution consists of autonomously detecting landmarks, self-localizing and performing the planned navigation actions. Advanced and innovative features, mostly related to exploration capabilities, navigation mode switcher, and the integrated aspect of our system distinguish it from the systems described in the literature.

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“…There are a number of methods used to increase the accuracy of the heading prediction. For example, odometery and range data can be combined to predict the heading [4]. There has been extensive work using cameras as a vision compass to detect the rotational motion of robots [5]- [7].…”

Section: Introductionmentioning

confidence: 99%