Abstract: Motion parameters estimation for localization of differential drive vehicles is an important part of robotics and autonomous navigation. Conventional methods require introceptive as well extroceptive sensors for localization. The present disclosure provides a control command based adaptive system and method for estimating motion parameters of differential drive vehicles. The method utilizes information from one or more time synchronized command signals and generate an experimental model for estimating one or more motion parameters of the differential drive vehicle by computing a mapping function. The experimental model is validated to determine change in the one or more motion parameters with change in one or more factors and adaptively updated to estimate updated value of the one or more motion parameters based on the validation. The system and method of present disclosure provide accurate results for localization with minimum use of extroceptive sensors.

Abstract: This disclosure relates generally to real-time path planning. Planning amidst obstacles in a cluttered indoor environment is a difficult task for a robotic agent. The disclosed method provides semidefinite programming induced free-space based path planning. Free-space is generated by an efficient environment grid resolution independent seeding technique. In the proposed resolution independent seeding technique, initial position of the robotic agent is considered as the first seed. For subsequent seeding, information of the expanded earlier seeds are employed intelligently. This process is followed unto a finite sequence, which naturally results in a contiguous navigable convex free-space. This contiguous navigable convex free-space is employed to create an undirected graph, which is then used for path planning. Path planning is done locally by evaluating the subgoal with respect to a final goal. Local planning cumulatively assists the planner to attain the final goal.

Abstract: Embodiments of the present disclosure provide systems and methods to eliminate (or filter) drift for dynamics model based localization of multirotors. The dynamics equations require drag modelling, which is dependent on velocity, to generate vehicles' acceleration along the body axis. The present disclosure considers the drag contribution, at velocity level, as a low frequency component. Incorrect or nonmodelling of this low frequency component leads to drift at velocity level. This drift can then be removed through a high pass filter to obtain drift free velocity data for pose estimation and better localization thereof.

Abstract: Motion parameters estimation for localization of differential drive vehicles is an important part of robotics and autonomous navigation. Conventional methods require introceptive as well extroceptive sensors for localization. The present disclosure provides a control command based adaptive system and method for estimating motion parameters of differential drive vehicles. The method utilizes information from one or more time synchronized command signals and generate an experimental model for estimating one or more motion parameters of the differential drive vehicle by computing a mapping function. The experimental model is validated to determine change in the one or more motion parameters with change in one or more factors and adaptively updated to estimate updated value of the one or more motion parameters based on the validation. The system and method of present disclosure provide accurate results for localization with minimum use of extroceptive sensors.

Abstract: This disclosure relates generally to real-time path planning. Planning amidst obstacles in a cluttered indoor environment is a difficult task for a robotic agent. The disclosed method provides semidefinite programming induced free-space based path planning. Free-space is generated by an efficient environment grid resolution independent seeding technique. In the proposed resolution independent seeding technique, initial position of the robotic agent is considered as the first seed. For subsequent seeding, information of the expanded earlier seeds are employed intelligently. This process is followed unto a finite sequence, which naturally results in a contiguous navigable convex free-space. This contiguous navigable convex free-space is employed to create an undirected graph, which is then used for path planning. Path planning is done locally by evaluating the subgoal with respect to a final goal. Local planning cumulatively assists the planner to attain the final goal.

Abstract: Robotic vision-based framework wherein an on-board camera device is used for scale estimation. Unlike conventional scale estimation methods that require inputs from more than one or more sensors, implementations include a system and method to estimate scale online solely, without any other sensor, for monocular SLAM by using multirotor dynamics model in an extended Kalman filter framework. This approach improves over convention scale estimation methods which require information from some other sensors or knowledge of physical dimension of an object within the camera view. An arbitrary scaled position and an Euler angle of a multirotor are estimated from vision SLAM (simultaneous localization and mapping) technique. Further, dynamically integrating, computed acceleration to estimate a metric position. A scale factor and a parameter associated with the multirotor dynamics model is obtained by comparing the estimated metric position with the estimated arbitrary position.

Abstract: Conventional techniques involve fusion of Inertial Measurement Units (IMU) sensor based method and vision based localization technique for localization of rotor systems. However vision based localization technique may be prone to errors due to motion blur, drastic lighting change, sudden rotation at UAV, and the like, while the drift in IMU based localization severely impact overall solution. Embodiments of the present disclosure provide systems and methods to eliminate (or filter) drift for dynamics model based localization of multirotors. The dynamics equations require drag modelling, which is dependent on velocity, to generate vehicles' acceleration along the body axis. The present disclosure considers the drag contribution, at velocity level, as a low frequency component. Incorrect or nonmodelling of this low frequency component leads to drift at velocity level. This drift can then be removed through a high pass filter to obtain drift free velocity data for pose estimation and better localization thereof.

Abstract: Robotic vision-based framework wherein an on-board camera device is used for scale estimation. Unlike conventional scale estimation methods that require inputs from more than one or more sensors, implementations include a system and method to estimate scale online solely, without any other sensor, for monocular SLAM by using multirotor dynamics model in an extended Kalman filter framework. This approach improves over convention scale estimation methods which require information from some other sensors or knowledge of physical dimension of an object within the camera view. An arbitrary scaled position and an Euler angle of a multirotor are estimated from vision SLAM (simultaneous localization and mapping) technique. Further, dynamically integrating, computed acceleration to estimate a metric position. A scale factor and a parameter associated with the multirotor dynamics model is obtained by comparing the estimated metric position with the estimated arbitrary position.