Radar presents a promising alternative to lidar and vision in autonomous vehicle applications, able to detect objects at long range under a variety of weather conditions. However, distinguishing between occupied and free space from raw radar power returns is challenging due to complex interactions between sensor noise and occlusion.To counter this we propose to learn an Inverse Sensor Model (ISM) converting a raw radar scan to a grid map of occupancy probabilities using a deep neural network. Our network is selfsupervised using partial occupancy labels generated by lidar, allowing a robot to learn about world occupancy from past experience without human supervision. We evaluate our approach on five hours of data recorded in a dynamic urban environment. By accounting for the scene context of each grid cell our model is able to successfully segment the world into occupied and free space, outperforming standard CFAR filtering approaches. Additionally by incorporating heteroscedastic uncertainty into our model formulation, we are able to quantify the variance in the uncertainty throughout the sensor observation. Through this mechanism we are able to successfully identify regions of space that are likely to be occluded.
Radar detects stable, long-range objects under variable weather and lighting conditions, making it a reliable and versatile sensor well suited for ego-motion estimation. In this work, we propose a radar-only odometry pipeline that is highly robust to radar artifacts (e.g., speckle noise and false positives) and requires only one input parameter. We demonstrate its ability to adapt across diverse settings, from urban UK to off-road Iceland, achieving a scan matching accuracy of approximately 5.20 cm and 0.0929 deg when using GPS as ground truth (compared to visual odometry's 5.77 cm and 0.1032 deg). We present algorithms for keypoint extraction and data association, framing the latter as a graph matching optimization problem, and provide an in-depth system analysis.
We consider the two-sided matching market with bandit learners. In the standard matching problem, users and providers are matched to ensure incentive compatibility via the notion of stability. However, contrary to the core assumption of the matching problem, users and providers do not know their true preferences a priori and must learn them. To address this assumption, recent works propose to blend the matching and multi-armed bandit problems. They establish that it is possible to assign matchings that are stable (i.e., incentive-compatible) at every time step while also allowing agents to learn enough so that the system converges to matchings that are stable under the agents' true preferences. However, while some agents may incur low regret under these matchings, others can incur high regret-specifically, Ω(T ) optimal regret, where T is the time horizon. In this work, we incorporate costs and transfers in the two-sided matching market with bandit learners in order to faithfully model competition between agents. We prove that, under our framework, it is possible to simultaneously guarantee four desiderata:(1) incentive compatibility, i.e., stability, (2) low regret, i.e., O(log(T )) optimal regret, (3) fairness in the distribution of regret among agents, and (4) high social welfare.1 Initialize matching M : A → A such that M (a) ← ∅ for all a ∈ A; 2 Initialize empty (FIFO) queues Q(p) ← [] for all p ∈ P;// Fill each provider's queue with users in order of decreasing preference. 3 for p ∈ P do 4 for i = 1, 2, . . . , N do 5 Append r −1 (i; V (p, •)) to Q(p); // Add p's i-th ranked user. 6 end 7 end // As long as there exists a provider who is unmatched... 8 while ∃p ∈ P : M (p) = ∅ do 9 u ← pop(Q(p)); // Provider p's favorite user of those remaining in p's queue. // If user u is unmatched, match u and p. 10 if M (u) = ∅ then 11 M (u) ← p; 12 M (p) ← u; // If user u prefers p to its current match M (u), match u and p.13 else if V (u, p) > V (u, M (u)) then 14 M ′ (M (u)) ← ∅; 15 M (u) ← p; 16 M (p) ← u; 17 end 18 Return M ;
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