From Scene Representation to Robust Autonomy: A seamless Integration

Abstract

The objective of this study is to examine the interdependence between scene representation and robust autonomy of mobile robots such as quadrotors and cars. It addresses crucial issues with the motion planning and SLAM components in the navigation system. Voxel Grids built from range sensors have been a popular choice of world representation for motion planning. The planners query the map for distance to collision and gradients, the planner uses these measurements to generate a smooth and safe trajectory through trajectory optimization. However, these methods assume the presence of zero sensor noise, such an assumption is severely violated when the maps are built through RGBD cameras, the noise in the point clouds are transferred to the occupancy maps through a non-linear transform and is further propagated to the distance and gradient field which results in a non-parametric noise. The performance of the deterministic planners that rely on such inaccurate gradients was found to decrease. As an antidote we propose CCO-VOXEL the very first chance-constrained optimization (CCO) algorithm that can compute trajectory plans with probabilistic safety guarantees in real-time directly on the voxel-grid representation of the world. We leverage the notion of Hilbert Space embedding of distributions and Maximum Mean Discrepancy (MMD) and gradient free Cross-Entropy Method for obtaining its minimum. We also show how a combination of low-dimensional feature embedding and pre-caching of Kernel Matrices of MMD allows us to achieve real-time performance in simulations as well as in implementations on on-board commodity hardware that controls the quadrotor flight. Voxel maps are not robust to outliers and fail to capture the world geometry when built from a highly noisy and sparse point cloud, such as those generated from triangulation through monocular visualinertial SLAM (VI-SLAM). Inaccurate maps like these can significantly decrease the performance of the motion planner. We propose UrbanFly , a quadrotor planning framework for navigation in a planar urban high-rise environment. The core importance of UrbanFly is its ability to handle sparse and noisy point clouds built from VI-SLAM. Based on the insight that the sky-scrappers are planar, we derived an Uncertainty-Integrated Cuboidal (UIC) representation of the world and developed an uncertaintyaware planner that can interpret and leverage the benefits of UIC to generate safe trajectories in real time. UrbanFly demonstrates a 3.5 times improvement in safety metrics compared to state-of- the-art algorithms. Points clouds generated from LiDAR need a lot of memory to store and transfer and lack structure, making it hard to extract spatial features. This makes it challenging to deploy lidar based Loop Detection and Closure (LDC) for distributed collaborative SLAM. To address this, we present FinderNet, a 6-DOF vi vii distributed LDC system. We develop a novel DEM representation to reveal the underlying geometric structure of the point clouds, and an auto-encoder decoder structure to compress the point clouds. LDC is performed in the compressed space, allowing deployment in a multi-agent setting. Our approach provides view-invariance through a process of canonicalization and differentiable alignment, which is different from existing methods that rely on feature aggregation or overlap estimation. FinderNet has been evaluated on real-world datasets such as Kitti, GPR, and Oxford Robot Car.