IIIT Hyderabad Publications |
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Robust SLAM and Path Planning under Uncertainty for Autonomous DrivingAuthor: Unni Krishnan R Date: 2022-07-29 Report no: IIIT/TH/2022/100 Advisor:Madhava Krishna AbstractThis thesis proposes solutions to two major problems (highlighted) faced when developing a selfdriving car using traditional modular approaches. That is to sense, percieve, localize/map, predict and plan/control. The first one is doing real-time Simultaneous Localization and Mapping SLAM at low sampling rates and/or at high linear or angular velocities. The keynote of the work showcases SROM’s ability to maintain localization at low sampling rates or at high linear or angular velocities where most popular LiDAR-based localization approaches get degraded fast. We also demonstrate SROM to be computationally efficient and capable of handling high-speed maneuvers. It also achieves low drifts without the need for any other sensors like IMU and/or GPS. Our method has a two-layer structure wherein first, an approximate estimate of the rotation angle and translation parameters are calculated using a Phase Only Correlation (POC) method. Next, we use this estimate as an initialization for a point-to-plane ICP algorithm to obtain fine matching and registration. Another key feature of the proposed algorithm is the removal of dynamic objects before matching the scans. This improves the performance of our system as the dynamic objects can corrupt the matching scheme and derail localization. Our SLAM system can build reliable maps at the same time generating high-quality odometry. We exhaustively evaluated the proposed method in many challenging highways/country/urban sequences from the KITTI dataset and the results demonstrate better accuracy in comparisons to other state-of-the-art methods with reduced computational expense aiding in real-time realizations. We have also integrated our SROM system with our in-house autonomous vehicle and compared it with the state-of-the-art methods like LOAM and LeGO-LOAM. The second one is the problem of an agent/robot with non-holonomic kinematics avoiding dynamic and static obstacles whose state and velocity noise are non-parametric. Additionally, there may be bounds/constraints on the configurational space of the robot in the form of lane/corridor boundaries. In this work we consider the problem of an agent/robot with non-holonomic kinematics avoiding dynamic and static obstacles. Additionally there may be bounds/constraints on the configurational space of the robot in the form of lane/corridor boundaries. The robot’s state and velocity noise, the lanes, the obstacles, and the robot’s control noise are modelled as non-parametric distributions as Gaussian assumptions of noise models are violated in real-world scenarios. Under these assumptions, we formulate a robust MPC that samples robotic controls effectively in a manner that aligns the robot to the goal state while avoiding obstacles and staying within the lane bounds under the duress of such non-parametric noise. In particular, the MPC incorporates a distribution matching cost that effectively aligns the distribution of the current collision cone to a certain desired distribution whose samples are collision-free. This cost is posed as a distance function in the Hilbert Space, whose minimization typically results in the collision cone samples becoming collision-free. We show tangible performance gains compared to methods that model the collision cone distribution by linearizing the Gaussian approximations of the original nonparametric state and obstacle distributions. We also show superior performance to methods that pose a chance constraint formulation of the Gaussian approximations of non-parametric noise without subjecting such approximations to further linearizations. The performance gain is shown both in terms of trajectory length and control costs that vindicates the efficacy of the proposed method. Finally, we show the proposed method being used to navigate with a non holonomic differential drive robot in real-time in a realistic setting in Gazebo with dynamic and static obstacles. To the best of our knowledge, this is the first presentation of non-holonomic collision avoidance of stationary obstacles, moving obstacles and lane constraints in the presence of non-parametric state, velocity, actuator and lane boundary noise models. Full thesis: pdf Centre for Robotics |
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