IROS 2019部分論文整理1

Fast Time-optimal Avoidance of Moving Obstacles for High-Speed MAV Flight

摘要

Abstract—In this work, we propose a method to efficiently compute smooth, time-optimal trajectories for micro aerial vehicles (MAVs) evading a moving obstacle. Our approach first computes an n-dimensional trajectory from the start-to
an arbitrary target state including position, velocity and acceleration. It respects input- and state-constraints and is thus dynamically feasible. The trajectory is then efficiently checked for collisions, exploiting the piecewise polynomial formulation. If collisions occur, viastates are inserted into the trajectory to circumvent the obstacle and still maintain imeoptimality. These viastates are described by position, velocity, and acceleration. The evaluation shows that the computational demands of the proposed method are minimal such that obstacle avoidance can begin within few milliseconds. Optimality of generated trajectories, combined with the ability for frequent online re-planning from non-hover initial conditions, make the approach well suited for evasion of suddenly perceived obstacles during fast flight.
在本文中，我們提出了一個方法使得無人機有效地計算一條平滑的，時間最優的軌跡來躲避移動的障礙。我們的方法首先計算了一個從起點到任意目標狀態的n維軌跡，包括位置，速度，加速度。它遵守了輸入和狀態約束，因此是動態可行的。這條軌跡利用分段多項式公式來有效地檢查碰撞。如果有碰撞出現，通過在軌跡中插入中間狀態（viastate）來避開障礙，同時保持時間最優性。這些中間狀態（viastates）由位置、速度和加速度來描述。評估結果表明，該方法的計算需求很小，可以在幾毫秒內開始避障。生成軌跡的最優性，結合從非懸停初始條件頻繁在線重新規劃的能力，使得該方法非常適合在快速飛行中躲避突然感知的障礙。

The code used in this work is open-source: http://www.ais.uni-bonn.de/videos/IROS_2019_Beul

Three contributions are:

1. generation of trajectories targeting only partially defined target states
2. fast computation of optimal trajectories that avoid a static or moving obstacle
3. evaluation of our method in simulation including profiling of computational requirements

Visual-based Autonomous Driving Deployment from a Stochastic and Uncertainty-aware Perspective

摘要

End-to-end visual-based imitation learning has been widely applied in autonomous driving. When deploying the trained visual-based driving policy, a deterministic command is usually directly applied without considering the uncertainty of the input data. Such kind of policies may bring dramatical damage when applied in the real world. In this paper, we follow the recent real-to-sim pipeline by translating the testing world image back to the training domain when using the trained policy. In the translating process, a stochastic generator is used to generate various images stylized under the training domain randomly or directionally. Based on those translated images, the trained uncertainty-aware imitation learning policy would output both the predicted action and the data uncertainty motivated by the aleatoric loss function. Through the uncertainty- aware imitation learning policy, we can easily choose the safest one with the lowest uncertainty among the generated images. Experiments in the Carla navigation benchmark show that our strategy outperforms previous methods, especially in dynamic environments.
端到端基於視覺的模仿學習在自動駕駛領域中已經得到廣泛應用，在應用訓練好的基於視覺的駕駛策略時，通常直接應用確定性指令，而不考慮輸入數據的不確定性。這種策略在真實世界中可能會帶來巨大的危險。在這篇文章中，我們使用了最近的真實-仿真（real-to-sim）流程即在使用訓練策略時，將測試世界圖像轉換回訓練域。基於轉換後的圖像，訓練後的模仿學習策略會輸出預測的動作和數據不確定性，該方法在動態環境中具有更加的表現。

Three main contributions：

1. We transfer the real driving image back to diverse images stylized under the familiar training environment through a stochastic generator so that the decision is made through multiple alternate options.
2. The uncertainty-aware imitation learning network provides a considerable way to make driving decisions, which improves the safety of autonomous driving, especially in dynamic environments.
3. We explain the aleatoric uncertainty from the view of the noisily labelled data samples.

Maximum Likelihood Path Planning for Fast Aerial Maneuvers and Collision Avoidance

摘要

Typically, autonomous navigation through a complex environment requires a continuous search on a graph generated by a k-connected grid or a probabilistic scheme. As the vehicle travels, updating the graph with data from onboard sensors is expensive as is the search on the graph especially if the paths must be kinodynamically feasible.
常規方法是基於圖的連續搜索，在飛行過程中，從機載傳感器的數據中更新圖和在圖上搜索具有動態可行性的路徑需付出昂貴的代價。
Our method models the environment differently in two separate regions. Obstacles are considered to be deterministically known within the sensor range and probabilistically known beyond the sensor range.
我們的方法對兩部分區域進行了不同的建模，障礙物在傳感器範圍內被認爲是確定性建模，在傳感器之外範圍的障礙物以概率性建模。
Instead of searching for the path with the lowest cost (typically the shortest path), the method maximizes the likelihood to reach the goal in determining the immediate next step for navigation.
與搜尋最低代價路徑不同的是，這個方法在決定當前時刻的下一步導航點時，最大化到達目標的可能性。

問題定義

SuMa++: Efficient LiDAR-based Semantic SLAM

In this paper, we propose an extension of a recently published surfelbased mapping approach exploiting three-dimensional laser range scans by integrating semantic information to facilitate the mapping process. The semantic information is efficiently extracted by a fully convolutional neural network and rendered on a spherical projection of the laser range data.

Our approach exploits ideas from a modern LiDAR SLAM pipeline [2] and incorporates semantic information obtained from a semantic segmentation generated by a Fully Convolutional Neural Network (FCN) [21]. This allows us to generate high-quality semantic maps, while at the same time improve the geometry of the map and the quality of the odometry.

The main contribution of this paper is an approach to integrate semantics into a surfel-based map representation and a method to filter dynamic objects exploiting these semantic labels. (將語義引入surfel-based mapping後,對比純surfel mapping有更好的動態性能.)

本文研究重點:In contrast to them, we focus on generating a semantic map with an abundance of semantic classes and using these semantics to filter outliers caused by dynamic objects (根據語義信息濾除動態目標), like moving vehicles and humans, to improve both mapping and odometry accuracy. (同時提高建圖和里程計精度)

整個流程將里程計估計和語義分割兩個部分進行整合，首先是將pointcloud傳進RangeNet++生成原始語義圖，然後使用flood-fill方法對原始語義圖進行處理得到優化後的語義圖。對世界模型和觀測模型進行語義一致性檢測來移除outliers，最後基於語義約束來完成ICP里程計估計。

本文特別之處在於Filtering Dynamics using Semantics部分，在介紹了傳統基於幾何方法SLAM的缺點後，引出we filter dynamics by checking semantic consistency between the new observation ${S_{D}}$ and the world model ${S_{M}}$, when we update the map.

基於語義分割的點雲結果進行ICP里程計估計，標準ICP問題構建與求解不詳細展開。本文貢獻點在於求解ICP時基於語義分割的結果考慮了動態物體的權重。

Reference:
[2] Efficient Surfel-Based SLAM using 3D Laser Range Data in Urban Environments.
[21] RangeNet++: Fast and Accurate LiDAR Semantic Segmentation.