[fix] handle an ICP term with no residuals in the depth solver - #156
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GPUICPTools::match_and_reduce wrote cost, rhs and hessian only inside its `count > 0` guards, so two reachable cases handed the caller back storage it never initialised: both counts zero, and photometric zero with point-to-point positive, where the second block accumulates with +=. VisualICP declares that storage as `Matrix6T H_icp; Vector6T rhs_icp;` and `float cost;`, so the values reached augmented_system.ldlt().solve() and could turn the LM step non-finite. Both blends also used fixed weights whether or not a term had residuals. Averaging an empty term in as a zero cost scores a pose that lost all of its matches as a perfect fit for that term, and count_photometric flips between LM iterations because magic_depth gates on lm_cam while count_p does not. A step that destroyed every photometric match therefore came back about 5x cheaper than it was and the rho > 0.25 test accepted it. Each blend now hands the whole weight to whichever term still has residuals. match_and_reduce and both VisualICP terms report whether they accepted anything, and the callers skip the blend instead of neutralising the outputs with a zero weight, which would not have been safe anyway since 0 * NaN is NaN. Also drops a dead `num_tracks != 0` guard, makes num_tracks const, and documents what match_and_reduce computes. This is not confirmed to be the cause of the reported point_to_point_kernel `assert(err >= 0)` failure. The chain is consistent with it, but the crash was never reproduced, and NaN depth reaching the kernels through the guards at window_matcher.cu:77/99/318 remains a separate unfixed candidate. Tested: new libs/cuda_modules/test/icp_tools_test.cpp; cuda_modules_test 46/46, cuvslam_math_test 9/9. Needs a dataset eval before merge - three of these are behaviour changes to the RGBD estimator. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
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Included review availability: Your plan provides up to 12 included reviews per hour; 11 remain after this review. 📝 WalkthroughWalkthroughThe change adds explicit residual-availability results to GPU ICP and Visual ICP calculations. It updates weighting, blending, and solver control for missing residuals. It adds CUDA-backed tests for empty, rejected, mixed, perfect-fit, and perturbed-pose cases. ChangesResidual availability handling
Priority: ➖ Normal Estimated code review effort: 3 (Moderate) | ~20 minutes Merge Risk: ⚪ Minimal · up to The solver now rejects empty residual sets instead of accepting them as zero-cost fits, while preserving valid single-term and perfect-fit behavior. No concrete current-head merge-blocking risk remains. Sequence Diagram(s)sequenceDiagram
participant total_cost_and_hessian
participant reprojection_cost_and_hessian
participant icp_hessian_and_cost
participant GPUICPTools
total_cost_and_hessian->>reprojection_cost_and_hessian: request reprojection cost and Hessian
reprojection_cost_and_hessian-->>total_cost_and_hessian: return availability and cost
total_cost_and_hessian->>icp_hessian_and_cost: request ICP cost and Hessian
icp_hessian_and_cost->>GPUICPTools: match and reduce residuals
GPUICPTools-->>icp_hessian_and_cost: return availability and reduced outputs
icp_hessian_and_cost-->>total_cost_and_hessian: return availability and cost
total_cost_and_hessian->>total_cost_and_hessian: blend available residual terms
🚥 Pre-merge checks | ✅ 4 | ❌ 1❌ Failed checks (1 warning)
✅ Passed checks (4 passed)
✨ Finishing Touches 💡 1📝 Generate docstrings 💡
🧪 Generate unit tests (beta)
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Inline comments:
In `@libs/pnp/visual_icp.cpp`:
- Line 151: Propagate aggregate residual availability from
total_cost_and_hessian, combining the reprojection result with the ICP result so
have_reprojection is not lost when either term is empty. In solve_level, return
before forming or evaluating the LM step when no term reports residuals, while
preserving zero-cost handling for valid residual sets.
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Test Results
cuVSLAM Evaluation KPIs
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total_cost_and_hessian computed have_reprojection and have_icp but returned only the cost, so the aggregate answer to "did anything produce a residual?" was thrown away. With neither term reporting one - no landmark in front of the camera, and either no depth info or an empty ICP term - H, rhs and cost were all the empty sum, and solve_level walked into the LM loop on that system anyway. prr then came out as 0/0, so rho was NaN, every iteration fell through to lambda *= 2, and the loop burned its whole budget re-evaluating a system with no data in it - on the depth path that is a GPU reduction per iteration. It ended with current_cost <= initial_cost as 0 <= 0, so the level reported success and handed back an all-zero information matrix, which SolverSfMRGBD::solveNextFrame latched into prev_static_info_exp_ while counting the frame as tracked. total_cost_and_hessian now returns whether either term had residuals, with cost as an out parameter to match the two term helpers, and solve_level returns false with a zeroed static_info_exp before forming a step when it did not. The caller already handles false correctly: it keeps the previous pose and restores the previous information matrix. The gate reads the flag and never the cost, so a residual set that happens to fit perfectly still goes through the loop. The same guard now covers the candidate evaluation inside the loop. A step that throws away every residual scored a cost of zero, which put rho at about 1/prr and got the step accepted as a perfect fit - the failure mode 75f67c9 fixed for the individual terms, one level up. Such a candidate is now rejected and the next step shortened. Tested: new libs/pnp/test/visual_icp_test.cpp, which needed the pnp test target to build under USE_CUDA and not only USE_CUNLS. Its empty-residual case fails on the parent commit and passes here; the perfect-fit and perturbed-pose cases pass both sides, so the shortcut does not fire on data the solver can use. Full ctest 18/18. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
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Inline comments:
In `@libs/pnp/test/visual_icp_test.cpp`:
- Around line 115-131: Update solve_level in VisualICP to guard the LM
gain-ratio calculation when current_cost is non-positive, returning success for
the level while preserving the zero cost. Ensure prr and rho are not computed
for a perfect fit, while retaining the existing behavior for positive-cost
problems.
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solve_level formed the gain ratio by dividing by current_cost. A level that starts at, or converges to, a perfect fit has every residual at exactly zero, so cost and rhs are both zero and prr came out as 0/0. NaN then disabled the two tests that read it. The prr < sqrt_epsilon() convergence break never fired, because every comparison against NaN is false, and rho failed the rho > 0.25 test, so each pass fell through to lambda *= 2. The level burned its whole iteration budget - twenty passes - re-solving a problem it had already solved, and each pass calls total_cost_and_hessian, which on the depth path is a GPU reduction. The answer was right by accident: static_info_exp = H was never touched and 0 <= initial_cost held, so the pose survived and the level reported success. The loop now breaks when current_cost is not positive, ahead of the augmented system, so the pointless LDLT and guess evaluation go too and not just prr and rho. Breaking lands in the existing tail, which keeps the pose, reports the information those residuals carry and returns success with the zero cost intact, so the observable contract does not change. The guard is <= 0 rather than a threshold. ComputeHuberLoss is non-negative for any x_squared >= 0 and the two terms blend as a convex combination, so current_cost reaching zero means a perfect fit and nothing else - it cannot fire on a positive-cost problem. The commented-out initial_cost < 5e-3f exit just above the loop stays commented out; that one is a threshold, and disabling it was a deliberate call about the pendulum effect on still frames. Tested: pnp_test 3/3, full ctest 18/18. SolveRecoversAPerturbedPose still converges to within a tenth of its starting error, so positive-cost problems are untouched, and SolveAcceptsAPerfectFit dropped from 3 ms to 0 ms now that the loop exits on the first pass. No new test - the fix preserves the observable result on purpose, so nothing reachable through the public API separates the two states. That existing test grew an allFinite() assertion on the information matrix and a note that this path has to answer without scoring a gain ratio. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
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GPUICPTools::match_and_reduce wrote cost, rhs and hessian only inside its
count > 0guards, so two reachable cases handed the caller back storage it never initialised: both counts zero, and photometric zero with point-to-point positive, where the second block accumulates with +=. VisualICP declares that storage asMatrix6T H_icp; Vector6T rhs_icp;andfloat cost;, so the values reached augmented_system.ldlt().solve() and could turn the LM step non-finite.Both blends also used fixed weights whether or not a term had residuals. Averaging an empty term in as a zero cost scores a pose that lost all of its matches as a perfect fit for that term, and count_photometric flips between LM iterations because magic_depth gates on lm_cam while count_p does not. A step that destroyed every photometric match therefore came back about 5x cheaper than it was and the rho > 0.25 test accepted it. Each blend now hands the whole weight to whichever term still has residuals.
match_and_reduce and both VisualICP terms report whether they accepted anything, and the callers skip the blend instead of neutralising the outputs with a zero weight, which would not have been safe anyway since 0 * NaN is NaN.
Also drops a dead
num_tracks != 0guard, makes num_tracks const, and documents what match_and_reduce computes.This is not confirmed to be the cause of the reported point_to_point_kernel
assert(err >= 0)failure. The chain is consistent with it, but the crash was never reproduced, and NaN depth reaching the kernels through the guards at window_matcher.cu:77/99/318 remains a separate unfixed candidate.Tested: new libs/cuda_modules/test/icp_tools_test.cpp; cuda_modules_test 46/46, cuvslam_math_test 9/9. Needs a dataset eval before merge - three of these are behaviour changes to the RGBD estimator.
Summary by CodeRabbit
Bug Fixes
Tests