Fast Monte-Carlo Localization on Aerial Vehicles using Approximate Continuous Belief Representations

Fast Monte-Carlo Localization on Aerial Vehicles using Approximate Continuous Belief Representations


Size, weight, and power constrained platforms impose constraints on computational resources that introduce unique challenges in implementing localization algorithms. We present a framework to perform fast coarse localization on such platforms enabled by the compressive capabilities of Gaussian Mixture Model representations of point cloud data. Given raw structural data from a depth sensor and pitch and roll estimates from an on-board attitude reference system, a multi-hypothesis particle filter localizes the vehicle by exploiting the likelihood of the data originating from the mixture model. We demonstrate analysis of this likelihood in the vicinity of the ground truth and detail its utilization in a particle filter-based vehicle localization strategy, and later present results of real-time implementations on a commercial off-the-shelf embedded platform that outperform localization results from running a state-of-the-art algorithm, ORB-SLAM2, on the same environment.


1 Supplementary Material

Representative video:

2 Introduction

Figure 1: Comparison of the mean particle filter pose (orange) with that of the integrated process model trajectory (cyan) from a representative (office) dataset. The filter estimate is initialized with a uniform distribution away from the true location of the vehicle (and thus is incorrect). As the camera observes more informative data the filter quickly converges to the correct pose. Top Right: Four views of the raw point cloud sensor data and the corresponding view of the GMM map from the mean of the particle filter estimates. The GMM components are superimposed on top of the source point cloud with their bounds.

For an agent lost in a known environment, much of the cost of localization can be offset by precomputing measures of what the sensor is expected to see with localization then cast as the much simpler problem of a search through these pre-existing “hallucinated” views. However, exhaustively considering all possible views incurs a prohibitive cost that increases exponentially with both the dimensionality of the state space and the environment volume extents. Further, naïve pre-rendering approaches can be susceptible to errors caused by perceptual aliasing due to slight variations in the environment appearance or by regions that are not feature rich such as blank walls [1].

In this paper we present a framework enabling rapid computation of sensor data likelihood via an environment representation parameterization that is both high-fidelity and memory efficient. The framework models a depth camera observation as the projection of the environment representation into image space with a likelihood measure that varies smoothly about the true camera pose. We thus exploit the dramatically reduced storage complexity of the representation and the local spatial regularity of a fast-to-compute likelihood function to re-cast the pose estimation problem as that of Monte-Carlo localization [13].

We pursue the problem of 6 Degree-of-Freedom coarse pose estimation problem given Size, Weight, and Power (SWaP) constrained micro air vehicles operating in a known environment with an onboard monocular depth camera, Inertial Measurement Unit (IMU), and a dense 3D pointcloud of the environment. We assume that the the vehicle pitch and roll are obtained from an attitude estimation algorithm or provided by the IMU to constrain the search space to just heading and position in real-time.

Prior works exploiting known map appearance for monocular pose estimation [12, 10, 8, 4] employ a textured depth map within an iterative optimization framework to compute the warp that minimizes a photometric cost function between a rendered image and the live image such as the Normalized Information Distance[10], that is robust to illumination change, and a Sum of Squared Differences cost function with an affine illumination model to tackle illumination change[8]. Both algorithms rely on initialization for tracking via a GPS prior or an ORB-based bag-of-words approach, respectively, and textured data for refinement. In the latter, the authors initialize the algorithm via an exhaustive search over all keyframes generated according to a heuristic that scales at best quadratically with increasing data.

We propose a localization strategy that generates expected sensor observations via an analytic projection of a finite number of mixture components into the camera frame, yielding a fast pre-rendering of a large number of potential candidate locations (as compared to approaches that render the expected dense image). These rendered projection views enable the computation of the likelihood of the sensor observations arising for a hypothesized pose. Note that in contrast to the above mentioned algorithms that use RGB information, we only use the depth observations from our sensors. Contributions of this work include:

  • A localization formulation based on a memory efficient, high fidelity environment model;

  • Fast computation of the likelihood of sensor data originating from a pose given this representation;

  • A particle filter-based localization strategy with adaptive importance sampling based on this fast likelihood calculation; and

  • A parallel formulation with experimental evaluation on a mobile GPU system.

3 Approach

Contemporary direct tracking algorithms require the projection of high dimensional dense (or semi-dense) points into the image space to align the current sensor data to a model. In contrast, we employ GMMs as succinct parameterized representations that avoid raycasting high dimensional data via an analytic projection into image space leading to a substantial reduction in input and complexity. The consequent orders of magnitude computational savings (hundreds of components vs many thousands of 3D points) enables projection to multiple pose hypotheses concurrently in real-time and motivates this work.

This section details the choice of environment representation and how it enables the proposed real-time sensor data likelihood computation.

3.1 Spatial GMMs as an Environment Representation for Tracking

Conventional means of representing maps such as voxel grids discretize space and encode occupancy leading to resolution dependent model fidelity and memory efficiency. An alternate approach to representing occupancy (or the existence of matter in space) is to use a Hierarchical Gaussian Mixture Model (HGMM) that attempts to approximate the underlying distribution from which sensor measurements are sampled. This formulation is capable of representing the underlying environment model with as high a fidelity as required and gracefully scales in fidelity to avoid information loss [11]. Additionally, HGMM representations provide a probabilistic uncertainty estimate of the occupancy at any sampled location. Fitting these models to data in real time is possibledue to recent advances that enable efficient operation [2]. For the purpose of this paper, we limit the discussion to using only GMM maps of a high fidelity, but note that the approach can be readily extended to a hierarchical formulation.

Figure 2: Negative log-likelihood plots of sensor data acquired from camera poses offset from the true pose by linear and rotational displacements compared when utilizing all the Gaussian components present in the model vs when utilizing only the relevant components using the approximation discussed in Sec. 4

3.2 Projection into Image Space

Given an undistorted depth image acquired from a sensor at time , we are interested in finding the pose of the camera with respect to a prior map of the environment assuming a standard pinhole camera projection.

A spatial GMM represents the probability of matter existing at a specific 3D location given the model component parameters such that


where is the mixture weight, the mean 3D position, and the covariance of the component of the GMM respectively, with and . This GMM is projected to image space by a transformation into the camera coordinate frame followed by a pinhole camera projection.

A point in the world frame is transformed into the camera frame by and denoted as

The operator is expressed as a composition of the input point with the corresponding rotation matrix and translation

Consider a sample and a monotonic continuous nonlinear function of , (where and are in the same space). The first order Taylor’s series expansion about a point leads to

Thus, about the point (after dropping superscripts for brevity),

and by applying linearity of expectation

This transformed random variable is then projected to the image space with the pinhole projection operation on a point :

where is the focal length of the camera, and and are the principal point offsets. The derivative of the projection operation with respect to the 3D point is

and the projection of a 3D normal distribution into image space is a 2D normal distribution


Thus, for a GMM, which is simply a linear combination of 3D Normal distributions, the corresponding belief distribution in image space for camera pose is


where estimates the probability of the depth at a particular pixel location having originated from the component per Eq. 2, is a random variable signifying the depth at a certain pixel, and is the number of componenents in the GMM model .

3.3 Estimating the Likelihood of a Camera Pose Hypothesis

As shown in the previous subsection, the projection of the spatial GMM constitutes a corresponding GMM in image space. Thus, each projected GMM component represents a probability support region for pixels in the corresponding spatial extent being that intensity (here depth). Since the contribution of multiple components may be very small, and to avoid numerical underflow, we utilize the negative log likelihood of the sensor data having originated from those components [2]. Given a scan of depth pixels from a sensor scan and a set of GMM parameters , the log likelihood is calculated as


where is the normal distribution presented in Eq. 2, and is the number of pixels in the sensor scan. In a local neighbourhood, this likelihood should peak at the true pose of the camera and fall off as we move further away. This is indeed the case in practice, as shown in Fig. 2.

Figure 3: Rapid Localization overview. The algorithm operates on depth image streams and a source of odometry given a precomputed GMM map of the environment. Using the current particle hypotheses, the GMM components are projected into the image space for each of these particles and their likelihood is computed. The likelihood is then used to weigh the particles prior to a resampling, followed by forward propagation based on odometry associated with the next depth image.

3.4 Tracking Multiple Hypotheses

The discussion above only considers the nature of the likelihood in the vicinity of the true location; in practice it is not reasonable to assume that a single viewpoint suffices to localize the system as perceptual aliasing may arise (due to a paucity of data that precludes state observability). Hence, we propose a technique that permits tracking of multiple hypotheses and ensures appropriate weighting of equally likely viewpoints given the current observations. In the case of perceptual aliasing, multiple observations are required prior to converging to an estimate.

A standard approach to tracking multiple hypotheses is a Monte Carlo filter (or particle filter). Particle filters operate by continuously sampling candidate particle poses and measure the likelihood of the current sensor data having originated at the sampled pose. Based on the relative scores of the samples the particles are resampled and propagated based on a process model (often a noisy source of odometry). Convergence is generally achieved as soon as the sequence of observations made over time render alternate hypotheses inadmissible. Note that due to their inherent structure, particle filters are extremely parallelizable, a fact that we exploit in our implementation.

State Propagation

We use a general first order Markov model and observation model in terms of conditional PDFs. We assume the presence of some odometry to drive the process model and inject Gaussian noise into it. This odometry can be obtained for instance from a vision based velocity estimator or IMU preintegration. Note that we assume that we know the pitch and roll that can be obtained for instance from the attitude and heading reference system on-board a robotic system.

Importance Weight

The particle filter we employ uses the negative log likelihood of the current scan being drawn from the model at the particle location as its importance weight. Thus, given the current state estimate of a particle out of particles at time step ,


where is the corresponding normalized importance weight obtained using the likelihood measurements for all the particles. Note that we use the inverse of the negative log likelihood function as our importance weight since negative log likelihood is minimum at the correct location.

Sampling Strategy

A particle filter should ideally converge to the correct hypothesis after running for a finite amount of iterations with a reduction in the filter variance signifying the confidence of the filter. At the same time, an early reduction in the filter variance may cause the filter to diverge to an incorrect hypothesis and never recover due to low variance. In order to avoid such situations, we implement the stratified sampling strategy [6] in combination with low variance sampling [13]. The particles are divided into random groups of equal weights and in each group we employ low variance sampling. This approach has low particle variance [13] and works well when the particle filter is tracking multiple hypotheses at once.

Handling Particle Deprivation

One of the most common failure modalities of a particle filter is that of particle deprivation [14]. Even with a large number of particles, the random nature of a particle filter might cause it to diverge from the correct state. Since the importance weights are an absolute measure of similarity between the current sensor scan and the model, the average of the importance weights signifies the probability of the filter tracking the true hypothesis. We employ the Augmented MCL strategy as described in [13]


where is the number of particles, is as defined in Eq. 5, and and are parameters used to smoothen out the absolute sum of sensor measurement probabilities over different time horizons.

Due to implementation limitations we cannot increase the number of particles once the filter is initialized and hence we choose to randomly reinitialize number of particles from the original set every iteration.

4 Fast Localization

In order to perform fast localization using the above approach it is essential to compute the likelihood of the data given a proposed pose as quickly as possible. Eq. 7 suggests that computing the likelihood of a scan having been sampled from the model is the summation of the contribution of all the components within the GMM. However, the key insight here is that not all the components have a significant contribution to the likelihood.

The pointclouds that we use in our experiments have roughly uniform coverage of points across the scene. As a consequence, all components fit to these pointclouds end up having roughly equivalent weighting factors. This fact in addition to the diminishing support of the normal distribution permits the approximation of using only the projected components in spatial proximity to a certain pixel location for likelihood calculation. Thus, the projected components of the model whose bound ellipsoids that do not lie within the sensor field of view are considered to have a negligible contribution to the likelihood computation 4. This enables the computation of the likelihood with a much smaller subset of the model therefore facilitating high computation speeds. As an added optimization step we perform this membership computation over subdivided patches of the image. These optimizations have negligible effect on the likelihood value of the sensor data, as demonstrated in Fig. 2.

In summary, we follow the following steps to obtain the relevant components for computing the likelihood:

  • Divide the image into 32 x 32 pixel patches;

  • Compute the 2D projection of each Gaussian component on to the image plane of the depth sensor;

  • Inflate the 2D ellipse of the projection of each component by a conservative radius (greater than or equal to half the diagonal of the patch) along its major and minor axis; and

  • For each patch, check if the center of the image patch lies within or on each of the inflated ellipses. All the ellipses which contain the center of the image patch are then used for likelihood computation for that patch.

Figure 4: Membership computation process. 3D Gaussian components from the GMM representation of the world are projected to the image plane. The image is subdivided into multiple patches, where for a selected patch the relevant Gaussian members are determined for computing the likelihood. In order to determine the latter, we employ the heuristic described in Sec. 4. For instance the inflated bounds of the bottom left projected component (red) do not contain the center of the selected patch; in contrast those of the bottom right (green) do, and the component is thus selected for computing the likelihood of data within that particular patch.

Given the set of these projected 2D Gaussian components and a scan, , the likelihood of the depth image can be computed with the reduced subset of components as


5 Results

5.1 Experiment Design

This section presents performance analysis of the filter on a variety of datasets. First, we conduct sensitivity analysis to determine the number of particles and the number of components we use in our implementation. Second, we analyze runtime performance of the filter and compare it with a state-of-the-art conventional tracking algorithm and show that the runtime is competitive both on a desktop class processor and on an embedded platform in-situ thus enabling SWaP constrained operation. Third, we analyze metric accuracy of the proposed filter on publicly available datasets and show that the filter output is close to ground truth. Fourth, we compare the localization performance of the filter with the state-of-the-art RGB-D visual SLAM algorithm on the same data and demonstrate superior performance for localization despite only using depth information from the sensor.

We evaluate the approach via

  • D1: The (a) “lounge” and (b) “copyroom” datasets [15];

  • D2: The voxblox dataset [9]; and

  • D3: A representative dataset.

In all cases we utilize a fixed number of components (as informed by [11]) to first fit a GMM to the pointcloud using the scipy3 toolkit.

We employ two processing systems for evaluation: (1) A Desktop with an Intel i7 CPU and a NVIDIA GTX 960 Ti GPU, and (2) An embedded NVIDIA TX2 module.

5.2 Sensitivity Analysis

Particle filters achieve increased performance with large number of particles at the cost of increased computational complexity. Conversely too few particles can lead to divergence from the true location due to an inability to represent the true underlying distribution. In order to find the appropriate number of particles that guarantee precision while being computationally feasible we compare the filter performance with a ground truth particle filter (). Assuming the underlying distribution represented by the particle set to be Gaussian, we compute the variance of the KL-Divergence [5] of multiple runs of the filter output with that of the ground truth filter to determine the empirically optimal parameters to be used in our implementation. A low value of the KL-Divergence indicates similar performance to the ground truth.

We first conduct a Particle Count Sensitivity test by varying the number of particles to assess filter performance and repeatability while ensuring computational feasibility. We then perform similar experiments with a fixed number of particles () obtained from the previous study with varying Gaussian components used to represent the ground truth map. These experiment motivate the design choice for the maximum number of particles and components in our implementation detailed in Table 1.

Figure 5: Left: Log of KL-Divergence between the ground truth filter and filters with reduced particle counts. A knee point at approximately 1000 particles suggests similar performance to the ground truth filter at or above . Right: A similar comparison given a ground truth map with many components () and maps with reduced number of components motivates the choice of a fixed number of components (with ) as detailed in the experiment design.
Variance Noise N
x 4 0.02
y 4 0.02
z 2 0.02
yaw 0.3 0.01
x 4 0.025
y 4 0.025
z 2 0.025
yaw 0.3 0.1
Table 1: List of parameters used

5.3 Runtime Performance Analysis

As seen in Fig. 6 the likelihood computation is the most computationally expensive operation, running at approximately 120 Hz on the Desktop and at 14 Hz on the TX2. The timings for likelihood computation vary with the number of Gaussian members used to compute likelihood for each image patch and therefore is dependent on the model and the scene.

The filter runs at an average rate of 80 Hz and 9.5 Hz on the Desktop and embedded class systems, respectively. This is comparable to the ORB-SLAM2 rates of 47 Hz and 20 Hz on the respective platforms.

Figure 6: Execution time comparison for subcomponents of the algorithm for the D1(a) dataset on an Intel i7 desktop with a NVIDIA GPU and an embedded NVIDIA TX2 GPU. The likelihood calculation is computationally the most expensive operation. The plots suggest that performance scales linearly with the number of CUDA cores. As a point of comparison ORB-SLAM2 runtime on the same dataset is faster on the embedded platform, but slower on the desktop.

5.4 Metric Accuracy Analysis

In this subsection we discuss results of the localization accuracy of the filter. As mentioned in Sec. 4 since we do not add new particles when the filter observes particle deprivation and instead randomly reinitialize the particles from the original set, the Root Mean Squared Error (RMSE) of the filter estimate increases when the filter observes particle deprivation. This is highlighted in the plots as vertical shaded regions. For all our evaluations we run the filter 10 times on each dataset and report the average of the mean filter estimate.

Evaluation with Ground Truth Datasets (D1, D2)

The objective of using these datasets is to demonstrate the ability of the filter to converge to correct results given ground truth odometry. We generated a GMM map of the environments using the reconstructed point cloud and used the delta position between two consecutive reported sensor poses as our process model. In all these experiments, we initialized the particles from a uniform distribution in a cube and yaw orientation around the known initial location. D1(a) and D1(b) contain nominal motion of the sensor, while D2 consists of very aggressive motion in all degrees of freedom. Additionally, in 8 we also analyze the impact of the slower runtime performance on the TX2 compared to the Desktop.

The filter estimate converged to an incorrect hypothesis for some runs in the initial iterations due to the highly symmetric nature of the environments but managed to snap back to the correct pose as soon as more distinguishing sensor data was observed.

Figure 7: Mean trajectory (red) of the particle filter estimate of 10 trials on the D1(a) dataset compared to the process model trajectory. The shaded region around the mean trajectory shows the variance of the filter estimate over multiple runs. The filter estimate has high variance in the beginning of the trajectories, but soon converges to the correct location and tracks the ground truth trajectory (blue).
Figure 8: Comparison of the filter performance on the Desktop with the NVIDIA TX2 on the D1(a) dataset. As the filter operates at a slower frame rate on the TX2, it initially exhibits a larger error than the Desktop version, but once the sensor observes a uniquely identifiable location, both trial sets converge to the ground truth sensor location.

The RMSE magnitude is higher for the D2 dataset than the other two as seen in Fig. [9].

Figure 9: RMSE of 10 trials of the particle filter on the D1(a),D1(b), and D2 datasets respectively. The region in red indicates the time at which the particle filter observes particle deprivation and a consequential RMSE rise.

Evaluation with Representative Dataset (D3)

The objective of using this dataset is to demonstrate results on a real-world application of the filter. We no longer use ground truth odometry. Additionally, since we don’t have a baseline algorithm to directly compare against, we compare the localization performance against a state-of-the-art RGB-D visual SLAM algorithm (ORB-SLAM2 [7]) that builds its own succinct map representation. Finally, we also briefly contrast the performance of the filter on the same dataset.

We generate a ground truth pointcloud and sensor data using a standalone FARO Focus 3D Laser scanner 4 and an ASUS Xtion RGB-D camera respectively. An IMU strapped to the camera determines the roll and pitch of the sensor. For odometry, we only use the frame to frame relative transform as opposed to the global pose output from ORB-SLAM2 as input to the process model.

As ground truth is not available for this dataset, we report the likelihood values at the mean particle filter location and the likelihood values at the reported ORB-SLAM2 poses. We also show a selection of views of the predicted sensor measurements at the mean filter and ORB-SLAM2 estimated poses, respectively. We performed multiple runs starting at different locations in the same environment and report results for the same. The particles in these experiments are initialized from a uniform distribution over a for position and a yaw volume given the dimensions of the environment.

An advantage of using particle filters over maximum likelihood estimatros is the fact that the filter can converge to the correct result even after moving through a region of low observability. To demonstrate this we also evaluated performance given an ill-conditioned dataset where the sensor transitions from a region where the GMM map is unavailable to a region with adequate information for localization prior to returning to the same impoverished region (Fig. 12).

We observe that the sensor measurements register at the converged filter location better than those for the ORB-SLAM2 estimate, as can be seen in Fig. 10.

Figure 10: Comparison of registration of current sensor measurement with ground truth point cloud (gray) in ORB-SLAM2 frame (cyan) and in the estimated filter frame (orange). The sensor measurement aligns with the ground truth point cloud the filter estimate frame while accumulated drift in the ORB-SLAM2 frame leads to poor alignment.

For testing the robustness, as seen in Fig. 11, the filter estimate diverges from the ORB-SLAM2 estimate but eventually converges to the correct estimate while ORB-SLAM2 drifts to an incorrect location.

Figure 11: Comparison of negative log likelihood values of a sensor scan given the ORB-SLAM2 estimate and the corrected filter estimate (Bottom) and a comparison between the ORB-SLAM2 filter position estimates (top) for three runs in D3. Note the increasing divergence of the ORB-SLAM2 as evidenced by the magnitude of the negative log likelihood while the filter estimate maintains a much lower value due to converging after moving through areas of low observability.
Figure 12: An input depth image (Left) collected from D3 along with the corresponding occluded GMM projection image from the same location (Right). The scene in the depth image is not available in the ground truth map.

6 Summary and Future Work

We present a framework to perform real-time localization of depth sensors given a prior continuous spatial belief representation. By utilizing a likelihood measure of the representation projected into image space we can perform robust particle filter localization in real-time even on an embedded GPU system. Key to being able to do this is the ability to render a much more succint representation into the image frame of the sensor to evaluate the likelihood of the data having originated from the given map at a given location.

The objective of this work is to provide a robust relocalization backend to bootstrap an optimization based precise localization strategy that incorporates additional data such as image intensity [3].

Part of the reason why it is not as straightforward to pursue an optimization based tracking algorithm with this map representation is the emphasis on it being approximately continuous. For Gaussian components that are more flat, the gradient profile can vary rapidly in the vicinity of the components, leading to poor conditioning that can challenge traditional iterative Gauss-Newton descent strategies. Further, the absence of strong gradient information in spatial data as encoded by the necessarily smooth-by-construction representation hinders the application of these methods bu is more robust to changes in local environment.

Future steps will pursue further formulation, optimization and incorporation as a backend for a visual SLAM algorithm running onboard a relevant SWaP constrained aerial platform.


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