This paper presents the perception, mapping, and planning pipeline implemented on an autonomous race car. It was developed by the 2019 AMZ driverless team for the Formula Student Germany (FSG) 2019 driverless competition, where it won 1st place overall. The presented solution combines early fusion of camera and LiDAR data, a layered mapping approach, and a planning approach that uses Bayesian filtering to achieve high-speed driving on unknown race tracks while creating accurate maps. We benchmark the method against our team's previous solution, which won FSG 2018, and show improved accuracy when driving at the same speeds. Furthermore, the new pipeline makes it possible to reliably raise the maximum driving speed in unknown environments from 3~m/s to 12~m/s while still mapping with an acceptable RMSE of 0.29~m.
Mobile manipulation is usually achieved by sequentially executing base and manipulator movements. This simplification, however, leads to a loss in efficiency and in some cases a reduction of workspace size. Even though different methods have been proposed to solve Whole-Body Control (WBC) online, they are either limited by a kinematic model or do not allow for reactive, online obstacle avoidance. In order to overcome these drawbacks, in this work, we propose an end-to-end Reinforcement Learning (RL) approach to WBC. We compared our learned controller against a state-of-the-art sampling-based method in simulation and achieved faster overall mission times. In addition, we validated the learned policy on our mobile manipulator RoyalPanda in challenging narrow corridor environments.
In this paper, we present a path planner for low-altitude terrain coverage in known environments with unmanned rotary-wing micro aerial vehicles (MAVs). Airborne systems can assist humanitarian demining by surveying suspected hazardous areas (SHAs) with cameras, ground-penetrating synthetic aperture radar (GPSAR), and metal detectors. Most available coverage planner implementations for MAVs do not consider obstacles and thus cannot be deployed in obstructed environments. We describe an open source framework to perform coverage planning in polygon flight corridors with obstacles. Our planner extends boustrophedon coverage planning by optimizing over different sweep combinations to find the optimal sweep path, and considers obstacles during transition flights between cells. We evaluate the path planner on 320 synthetic maps and show that it is able to solve realistic planning instances fast enough to run in the field. The planner achieves 14% lower path costs than a conventional coverage planner. We validate the planner on a real platform where we show low-altitude coverage over a sloped terrain with trees.
To autonomously navigate and plan interactions in real-world environments, robots require the ability to robustly perceive and map complex, unstructured surrounding scenes. Besides building an internal representation of the observed scene geometry, the key insight towards a truly functional understanding of the environment is the usage of higher-level entities during mapping, such as individual object instances. We propose an approach to incrementally build volumetric object-centric maps during online scanning with a localized RGB-D camera. First, a per-frame segmentation scheme combines an unsupervised geometric approach with instance-aware semantic object predictions. This allows us to detect and segment elements both from the set of known classes and from other, previously unseen categories. Next, a data association step tracks the predicted instances across the different frames. Finally, a map integration strategy fuses information about their 3D shape, location, and, if available, semantic class into a global volume. Evaluation on a publicly available dataset shows that the proposed approach for building instance-level semantic maps is competitive with state-of-the-art methods, while additionally able to discover objects of unseen categories. The system is further evaluated within a real-world robotic mapping setup, for which qualitative results highlight the online nature of the method.
Robotic platforms are emerging as a timely and cost-efficient tool for exploration and monitoring. However, an open challenge is planning missions for robust, efficient data acquisition in complex environments. To address this issue, we introduce an informative planning framework for active sensing scenarios that accounts for the robot pose uncertainty. Our strategy exploits a Gaussian Process model to capture a target environmental field given the uncertainty on its inputs. This allows us to maintain robust maps, which are used for planning information-rich trajectories in continuous space. A key aspect of our method is a new utility function that couples the localization and field mapping objectives, enabling us to trade-off exploration against exploitation in a principled way. Extensive simulations show that our approach outperforms existing strategies, with reductions of up to 45.1% and 6.3% in mean pose uncertainty and map error. We demonstrate a proof of concept in an indoor temperature mapping scenario.
Unmanned aerial vehicles (UAVs) represent a new frontier in a wide range of monitoring and research applications. To fully leverage their potential, a key challenge is planning missions for efficient data acquisition in complex environments. To address this issue, this article introduces a general informative path planning (IPP) framework for monitoring scenarios using an aerial robot. The approach is capable of mapping either discrete or continuous target variables on a terrain using variable-resolution data received from probabilistic sensors. During a mission, the terrain maps built online are used to plan information-rich trajectories in continuous 3-D space by optimizing initial solutions obtained by a course grid search. Extensive simulations show that our approach is more efficient than existing methods. We also demonstrate its real-time application on a photorealistic mapping scenario using a publicly available dataset.